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

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

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

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

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

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

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.

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