A Roadmap for Analyzing Data is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. Choosing appropriate statistical methods for your data is the single most important task you face and is at the heart of “doing statistics.” But this selection process is also the single most difficult thing you do when applying statistics! How, then, can you ensure that you have made an appropriate choice? By asking a series of questions, you can guide yourself to the appropriate choice of methods. Learning objectives: The questions to ask when choosing which statistical methods to use to conduct data analysis Rules for applying statistics in future studies and analyses This lecture discusses: How to choose the appropriate technique(s) for data analysis for both numerical and categorical variables Potential questions and the associated appropriate techniques for numerical variables Potential questions and the associated appropriate techniques for categorical variables In this File you will find: A Roadmap for Analyzing Data Lecture Power Point Presentation Test Bank for A Roadmap for Analyzing Data with 327 Questions with all answers to them 15 Exercises for A Roadmap for Analyzing Data seminar or lecture Plus reading resource on A Roadmap for Analyzing Data in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Business Analytics is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. Descriptive analytics, predictive analytics, and prescriptive analytics form the three broad categories of analytic methods. Descriptive analytics explores business activities that have occurred or are occurring in the present moment. Predictive analytics identifies what is likely to occur in the (near) future and finds relationships in data that may not be readily apparent using descriptive analytics. Prescriptive analytics investigates what should occur and prescribes the best course of action for the future. Predictive and prescriptive analytics make practical the use of big data to support decision making, although many of these techniques also work with smaller sets of data, as examples in this chapter demonstrate. This lecture begins with descriptive analytics but focuses on predictive analytics. The lecture does not cover prescriptive analytics methods. Learning objectives: To develop dashboard elements such as sparklines, gauges, bullet graphs, and tree-maps for descriptive analytics How to use classification and regression trees for predictive analytics How to use neural nets for predictive analytics How to use cluster analysis for predictive analytics How to use multidimensional scaling for predictive analytics In this File you will find: Business Analytics Lecture Power Point Presentation Test Bank for Business Analytics with 112 Questions with all answers to them 59 Exercises for Business Analytics seminar or lecture Plus reading resource on Business Analytics in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Time-Series Forecasting is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. A time series is a set of numerical data collected over time. Due to differences in the features of data for various investments described in the Using Statistics scenario, you need to consider several different approaches for forecasting time-series data. This lecture begins with an introduction to the importance of business forecasting and a description of the components of time-series models. The coverage of forecasting models begins with annual time-series data. First section presents moving averages and exponential smoothing methods for smoothing a series. This is followed by least-squares trend fitting and forecasting in the next section and autoregressive modeling in the following section. After lecture discusses how to choose among alternative forecasting models. Finally lecture develops models for monthly and quarterly time series. Learning objectives: To construct different time-series forecasting models: moving averages, exponential smoothing, linear trend, quadratic trend, exponential trend, autoregressive models, and least squares models for seasonal data To choose the most appropriate time-series forecasting model In this lecture we discussed: The importance of forecasting The component factors of the time-series model The smoothing of data series Moving averages Exponential smoothing Least square trend fitting and forecasting Linear, quadratic and exponential models Autoregressive models A procedure for choosing appropriate models Time-series forecasting of monthly or quarterly data by using dummy variables Pitfalls concerning time-series analysis In this File you will find: Time-Series Forecasting Lecture Power Point Presentation Test Bank for Time-Series Forecasting with 170 Questions with all answers to them 64 Exercises for Time-Series Forecasting seminar or lecture Plus reading resource on Time-Series Forecasting in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Multiple Regression Model Building is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. This lecture discusses multiple regression models with two independent variables. Moreover, this lecture considers regression models that contain more than two independent variables. Also lecture discusses model-building concepts that will help to develop the best model when confronted with a set of data that has many independent variables, such as the data to be collected at WSTA-TV. These concepts include quadratic independent variables, transformations of the dependent or independent variables, stepwise regression, and best-subsets regression. Learning objectives: To use quadratic terms in a regression model To use transformed variables in a regression model To measure the correlation among the independent variables To build a regression model using either the stepwise or best-subsets approach To avoid the pitfalls involved in developing a multiple regression model In this lecture we discussed: The quadratic regression model Using transformations in regression models - The multiplicative model - The exponential model Collinearity Model building - Stepwise regression - Best subsets The pitfalls & ethical considerations in multiple regression In this File you will find: Multiple Regression Model Building Lecture Power Point Presentation Test Bank for Multiple Regression Model Building with 96 Questions with all answers to them 39 Exercises for Multiple Regression Model Building seminar or lecture Plus reading resource on Multiple Regression Model Building in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Introduction to Multiple Regression is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. This lecture will introduce you to multiple regression models that use two or more independent variables to predict the value of a dependent variable. Learning objectives: • How to develop a multiple regression model • How to interpret the regression coefficients • How to determine which independent variables to include in the regression model • How to determine which independent variables are most important in predicting a dependent variable • How to use categorical independent variables in a regression model • How to predict a categorical dependent variable using logistic regression • How to identify individual observations that may be unduly influencing the multiple regression model In this File you will find: Introduction to Multiple Regression Lecture Power Point Presentation Test Bank for Introduction to Multiple Regression with 343 Questions with all answers to them 86 Exercises for Introduction to Multiple Regression Plus reading resource on Introduction to Multiple Regression in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resources with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Simple Linear Regression is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. This lecture discusses simple linear regression models that use a single numerical independent variable, X, to predict the numerical dependent variable, Y. In this lecture, you will learn regression analysis techniques that help uncover relationships between variables. regression analysis leads to selection of a model that expresses how one or more independent variables can be used to predict the value of another variable, called the dependent variable. regression models identify the type of mathematical relationship that exists between a dependent variable and an independent variable, thereby enabling you to quantify the effect that a change in the independent variable has on the dependent variable. Models also help you identify unusual values that may be outliers Learning objectives: How to use regression analysis to predict the value of a dependent variable based on a value of an independent variable The meaning of the regression coefficients b0 and b1 How to evaluate the assumptions of regression analysis and know what to do if the assumptions are violated To make inferences about the slope and correlation coefficient To estimate mean values and predict individual values In this File you will find: Simple Linear Regression Lecture Power Point Presentation Test Bank for Simple Linear Regression with 213 Questions with all answers to them 86 Exercises for Simple Linear Regression seminar or lecture Plus reading resource on Simple Linear Regression in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Analysis of Variance (ANOVA) is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. Analysis of variance, known by the acronym ANOVA, allows statistical comparison among samples taken from many populations. While ANOVA literally does analyze variation, the purpose of ANOVA is to reach conclusions about possible differences among the means of each group, analogous to the hypothesis tests of the previous chapter. Every ANOVA design uses samples that represent each group and subdivides the total variation observed across all samples (all groups) toward the goal of analyzing possible differences among the means of each group. How this subdivision, called partitioning, works is a function of the design being used, but total variation, represented by the quantity sum of squares total (SST), will always be the starting point. As with other statistical methods, ANOVA requires making assumptions about the populations that the groups represent. Learning objectives: The basic concepts of experimental design How to use one-way analysis of variance to test for differences among the means of several groups When and how to use a randomized block design How to use two-way analysis of variance and interpret the interaction effect How to perform multiple comparisons in a one-way analysis of variance, a randomized block design, and a two-way analysis of variance In this File you will find: Analysis of Variance Lecture Power Point Presentation Test Bank for Analysis of Variance with 213 Questions with all answers to them 61 Exercises for Analysis of Variance lecture / seminar Plus reading resource on Analysis of Variance in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file.

Chi-Square and Nonparametric Tests is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. This lecture extends hypothesis testing to analyze differences between population proportions based on two or more samples and to test the hypothesis of independence in the joint responses to two categorical variables. The lecture concludes with nonparametric tests as alternatives to several hypothesis tests. Learning objectives: How and when to use the chi-square test for contingency tables How to use the Marascuilo procedure for determining pairwise differences when evaluating more than two proportions How and when to use nonparametric tests How and when to use the McNemar test How to use the Chi-Square to test for a variance or standard deviation How to use the Friedman rank test for comparing multiple population medians in a randomized block design In this File you will find: Chi-Square and Nonparametric Tests Lecture Power Point Presentation Test Bank for Chi-Square and Nonparametric Tests with 175 Questions with all answers to them 59 Exercises for Chi-Square and Nonparametric Tests lecture / seminar Plus reading resource on Chi-Square and Nonparametric Tests in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file.

Two-Sample Tests is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. In this lecture, you will learn how to extend hypothesis testing to two-sample tests that compare statistics from samples selected from two populations. Learning objectives: • How to use hypothesis testing for comparing the difference between – The means of two independent populations – The means of two related populations – The proportions of two independent populations – The variances of two independent populations • The impact sample size can have on statistical significance • How to classify the effect size of a difference • When it is appropriate to consider the effect size in addition to statistical significance In this File you will find: Two-Sample Tests Lecture Power Point Presentation Test Bank for Two-Sample Tests with 210 Questions with answers 69 Exercises for Two-Sample Tests Plus reading resource on Two-Sample Tests in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Fundamentals of Hypothesis Testing: One-Sample Tests is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. In this lecture, you will learn several applications of hypothesis testing. You will learn how to make inferences about a population parameter by analyzing differences between the results observed, the sample statistic, and many more. Learning objectives: The basic principles of hypothesis testing How to use hypothesis testing to test a mean or proportion The assumptions of each hypothesis-testing procedure, how to evaluate them, and the consequences if they are seriously violated Pitfalls & ethical issues involved in hypothesis testing How to avoid the pitfalls involved in hypothesis testing In this File you will find: Fundamentals of Hypothesis Testing: One-Sample Tests Lecture Power Point Presentation Test Bank for Fundamentals of Hypothesis Testing: One-Sample Tests with 181 Questions with answers 78 Exercises for Fundamentals of Hypothesis Testing: One-Sample Tests Plus reading resource on Fundamentals of Hypothesis Testing: One-Sample Tests in order to enhance you overall knowledge about the topic. Once you will purchase this resource please leave a comment! All resources are compressed in zip file.

Confidence Interval Estimation is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. Suppose you want to estimate the mean GPA of all the students at your university. The mean GPA for all the students is an unknown population mean, denoted by u. You select a sample of students and compute the sample mean, denoted by X, to be 2.80. As a point estimate of the population mean, u, you ask how accurate is the 2.80 value as an estimate of the population mean, u? By considering the variability from sample to sample (see Section 7.2, concerning the sampling distribution of the mean), you can construct a confidence interval estimate for the population mean to answer this question. When you construct a confidence interval estimate, you indicate the confidence of correctly estimating the value of the population parameter, u. This allows you to say that there is a specified confidence that u is somewhere in the range of numbers defined by the interval. After studying this chapter, you might find that a 95% confidence interval for the mean GPA at your university is 2.75 < u < 2.85. You can interpret this interval estimate by stating that you are 95% confident that the mean GPA at your university is between 2.75 and 2.85. In this chapter, you learn to construct a confidence interval for both the population mean and population proportion. You also learn how to determine the sample size that is necessary to construct a confidence interval of a desired width. In this lecture, you learn to: To construct and interpret confidence interval estimates for the population mean and the population proportion To determine the sample size necessary to develop a confidence interval for the population mean or population proportion How to use confidence interval estimates in auditing When to use a finite population correction factor in calculating a confidence interval for either µ or π How to use a finite population correction factor in calculating a confidence interval for either µ or π How to use a finite population correction factor in calculating a sample size for a confidence interval for either µ or π The concept of bootstrapping and when it makes sense to use it. In this file you will find: Confidence Interval Estimation Lecture Power Point Presentation Confidence Interval Estimation Test Bank with 183 different related questions with full answer description and explanation 68 Exercises related to the topic with all answers to them Confidence Interval Estimation Reading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use counpon code during checkout process: LOVETOTEACH

Sampling Distributions is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. In many applications, you want to make inferences that are based on statistics calculated from samples to estimate the values of population parameters. In this lecture, you will learn about how the sample mean (a statistic) is used to estimate the population mean (a parameter) and how the sample proportion (a statistic) is used to estimate the population proportion (a parameter). In this lecture, you learn to: The concept of the sampling distribution To compute probabilities related to the sample mean and the sample proportion The importance of the Central Limit Theorem In this file you will find: Sampling Distributions Lecture Power Point Presentation Sampling Distributions Test Bank with 129 different related questions with full answer description and explanation 29 Exercises related to the topic with all answers to them Sampling Distributions Reading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

The Normal Distribution and Other Continuous is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. This lecture will help you learn about characteristics of continuous probability distributions and how to use the normal distribution to solve business problems. In this lecture, you learn to: To compute probabilities from the normal distribution How to use the normal distribution to solve business problems To use the normal probability plot to determine whether a set of data is approximately normally distributed To compute probabilities from the uniform distribution To compute probabilities from the exponential distribution In this file you will find: The Normal Distribution and Other Continuous Lecture Power Point Presentation The Normal Distribution and Other Continuous Test Bank with 178 different related questions with full answer description and explanation 53 Exercises related to the topic with all answers to them The Normal Distribution and Other Continuous Reading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Discrete Probability Distributions is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. This lecture introduces you to the concept and characteristics of probability distributions. Moreover, you will learn how the binomial, Poisson, and hypergeometric distributions can be applied to help solve business problems. A probability distribution for a discrete variable is a mutually exclusive list of all the possible numerical outcomes along with the probability of occurrence of each outcome. For more information look at the agenda of the lesson. In this lecture, you learn to: The properties of a probability distribution To compute the expected value and variance of a probability distribution To calculate the covariance and understand its use in finance To compute probabilities from binomial, hypergeometric, and Poisson distributions To use the binomial, hypergeometric, and Poisson distributions to solve business problems In this file you will find: Discrete Probability Distributions Lecture Power Point Presentation Test Bank with 171 different related questions with full answer description and explanation 65 Exercises related to the topic with all answers to them Defining and Collecting Data Reading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Basic Probability is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. The principles of probability help bridge the worlds of descriptive statistics and inferential statistics. Probability principles are the foundation for the probability distribution, the concept of mathematical expectation, and the binomial, Poisson, and hypergeometric distributions, topics. In this lecture you learn to: Basic probability concepts About conditional probability To use Bayes’ Theorem to revise probabilities Various counting rules In this file you will find: Basic Probability Lecture Power Point Presentation Test Bank with 171 different related questions with full answer description and explanation 65 Exercises related to the topic with all answers to them Defining and Collecting Data Reading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Numerical Descriptive Measures is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. Central tendency is the extent to which the values of a numerical variable group around a typical, or central, value. Variation measures the amount of dispersion, or scattering, away from a central value that the values of a numerical variable show. The shape of a variable is the pattern of the distribution of values from the lowest value to the highest value. This lecture discusses ways you can compute these numerical descriptive measures as you begin to analyze your data within the DCOVA framework. The lecture also talks about the covariance and the coefficient of correlation, measures that can help show the strength of the association between two numerical variables. In this lecture you learn to: To describe the properties of central tendency, variation, and shape in numerical data To construct and interpret a boxplot To compute descriptive summary measures for a population To calculate the covariance and the coefficient of correlation In this file you will find: Numerical Descriptive Measures Lecture Power Point Presentation Test Bank with 168 different related questions with full answer description and explanation 80 Exercises related to the topic with all answers to them Defining and Collecting Data Reading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Organizing and Visualizing Variables is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. When you organize the data, you sometimes begin to discover patterns or relationships in the data. To better explore and discover patterns and relationships, you can visualize your data by creating various charts and special displays. Because the methods used to organize and visualize the data collected for categorical variables differ from the methods used to organize and visualize the data collected for numerical variables, this lecture discusses them in separate sections. You will always need to first determine the type of variable, numerical or categorical, you seek to organize and visualize, in order to choose appropriate methods. This lecture also contains a section on common errors that people make when visualizing variables. When learning methods to visualize variables, you should be aware of such possible errors because of the potential of such errors to mislead and misinform decision makers about the data you have collected. In this lecture you learn to: To construct tables and charts for categorical data To construct tables and charts for numerical data The principles of properly presenting graphs To organize and analyze many variables In this file you will find: Organizing and Visualizing Variables Lecture Power Point Presentation Test Bank with 212 different related questions with full answer description and explanation 93 Exercises related to the topic with all answers to them Defining and Collecting DataReading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

Defining and Collecting Data is a lecture which is covered within the Statistic or Basic Business Statistic module by business and economics students. Nowadays, Data playing an important role in the success of the company. Without reliable and proper data most probably company will fail, while by possessing good data company may get a competitive advantage over main competitors in the market. It is extremely important for future managers to understand how to collect as well as to analyze data, as well as how to work with it. This teaching resource will help you to deliver Defining and Collecting Data lecture as well as seminar in interesting and professional way to your students. In this lecture you learn to: Understand the types of variables used in statistics Know the different measurement scales Know how to collect data Know the different ways to collect a sample Understand the types of survey errors In this file you will find: Defining and Collecting Data Lecture Power Point Presentation Test Bank with 204 different related questions with full answer description and explanation 45 Exercises related to the topic with all answers to them Two Interesting Case Studies with all answers to case study questions Defining and Collecting Data Reading Resources file in order to enhance Lecturer/Teacher/Student knowledge Once you will purchase this resource please leave a comment! All resources are compressed in zip file. You can purchase this teaching resource with more than 20 % Discount by pressing this link. Use coupon code during checkout process: LOVETOTEACH

MS Excel Applications is one of the topic which is covered during Mathematics lectures and seminars in order to enhance students knowledge. During this Lecture and Seminar we are covering following subtopics (Agenda): •Entering formulae •Sketching graphs •Descriptive statistics •Solving equations •Solving linear programming problems •Solving simultaneous linear equations In this File you will find: - 1 MS Excel Applications Lecture Power Point Presentation 17 Slides - 1 Seminar Plan - 13 Seminar Activities with full answer list for students All covered materials are taught for bachelor level students Level 3. Please write your comments once you purchase this lesson in order to have some suggestions for further improvements of teaching materials.

Linear Programming is one of the topic which is covered during Mathematics lectures and seminars in order to enhance students knowledge. During this Lecture and Seminar we are covering following subtopics (Agenda): •Graphing linear inequalities •Graphing systems of linear inequalities •Linear programming problems •Applications of linear programming In this File you will find: - 1 Linear Programming Lecture Power Point Presentation 23 Slides - 1 Seminar Plan - 14 Seminar Activities with full answer list for students All covered materials are taught for bachelor level students Level 3. Please write your comments once you purchase this lesson in order to have some suggestions for further improvements of teaching materials.