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

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

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.

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.

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.