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# Regression Analysis

(See attached file for full problem description) --- Q: The following data is given: Date Advertising Price Quantity January 25 12500 15 February 30 12200 17 March 26 11900 16 April 28 12000 18 May 27 11800 20 June 29 12500 18 July 28 11700 22 August 30 12100 15 September 24 1

### Outline - General Motors Economic Affect

Outline Forecasting is an essential tool in any decision making process. There are various techniques of forecasting. I Regression analysis is used to forecast revenues and expenses: Regression analysis is any statistical method where the mean of one or more random variables is predicted conditioned on other (measured) r

### How might you go about creating a stronger regression model?

How might you go about creating a stronger regression model? For example, you have a multiple regression model with an adjusted R-Square of 0.49, which would explain 49% of the variance of the dependent measure, and you would like to explain 70% of the variance of the dependent measure.

### Monthly sales forecasting

The Gametime Hat Company manufactures baseball caps that have various team logos in an assortment of designs and colors. The company has had monthly sales for the past 24 months as follows. Month Demand (1,000s) 1 8.2 2 7.5 3 8.1 4 9.3 5 9.1 6 9.5 7 10.4 8 9.7 9 10.2 10 10.6 11 8.2 12 9.9 13 10.3

### Dummy Variables in Regression

What are the effects of dummy variables in regression analysis and why are they useful?

### Case Study STA State Transport Authority: Dependant and independent varialbes - Demand Estimation

1.What is the dependent variable in this demand study? 2.What are the independent variables? 3. What are the expected signs of the variables thought to affect transit ridership? STA Data on Transit Ridership Year Weekly Riders (Y) (x 1,000) Price (P) per Ride (Cents) Population (T) (x 1,000 Income

### Regression and frequency tables.

Please find attached problem and excel worksheet that relates. --- (a) The regression line of y on x for n pairs of data has the equation . If the data is coded by means of the relations: what restrictions, if any, must be placed on and to ensure that the value of the regression coefficient b is unaffected?

### Questions and Problems

1. Regression analysis is a statistical procedure for developing a mathematical equation that describes how: a. one independent and one or more dependent variables are related b. several independent and several dependent variables are related c. one dependent and one or more independent variabl

### Find the correlation coefficient for the relationship between test results and sales

The store manager is under impression that his sales people do more business if they score higher on the social skills test which was administered this year. 5 victims were selected at random. Below, the results are X =test score, Y =weekly sales in \$1000. X (score) Y (sales) 4

### Regression Problem - Job Satisfaction

Work through a simple regression calculation using intrinsic job satisfaction and extrinsic job satisfaction. (intrinsic job satisfaction being the independant variable and extrinsic job satisfaction being the dependant variable) Represent the equation as y = bx + a. The data set for intrinsic and extrinsic job satisfaction

### Using Regression to Study Recent Loans of a Large Bank

A mortgage department of a large bank is studying its recent loans. Of particualr interest is how such factors as the value of the home (in thousands of dollars), education level of the head of the household, age of the head of the household, current monthly mortgage payment (in dollars), and gender of the head of the household

### Regression and Correlation

52. Refer to the Baseball 2000 data (Appendix D), which reports information on the 2000 Major League Baseball season. a. Let the games won be the dependent variable and total team salary, in millions of dollars, be the independent variable. Can you conclude that there is a positive association between the two variables? D

### Underlying average of a time series

When the underlying average of a time series is very stable and there is no trend, cyclical, or seasonal influences: a. A simple moving average forecast with n = 20 should outperform a simple moving average forecast with n = 3. b. A simple moving average forecast with n = 3 should outperform a simple moving average forecast wi

### Forecasting & Scatter Diagrams

I need help with these questions: An advantage of exponential smoothing over a simple moving average is that exponential smoothing requires one to retain less data. True False Which of the following statements about scatter diagrams is true? Time is always plotted on the Y axis. It can

### Multiple regressions and variables

Discussion of multiple regression with the topics from Dielman Terry 'Applied Regression Analysis' - A 2nd course in Bus. and Economic Statistics. Topics (title of chapters) to cover: - Multiple regression analysis - Fitting curves to data - Assessing the assumptions of the regression model - Using indicator and interact

### The Owner of Maumee Motors

The owner of Maumee Motors wants to study the relationship between the age of a car and its selling price. Listed below is a random sample of 12 used cars sold at Maumee Motors during the last year. A). If we want to estimate selling price based on the age of the car, which variable is the dependant variable and which is th

### Forecasting using moving averages and exponential smoothing

The following data represent 15 quarters of manufacturing capacity utilization (in percentages) QTR/YR Utilization 1/2000 82.5 2/2000 81.3 3/2000 81.3 4/2000 79.0 1/2001 76.6 2/2001 78.0 3/2001 78.4 4/2001 78.0 1/2002 78.8 2/2002 78.7

### Survival, correlation, and regression

1. Variables x and y each have standard deviations of 20. Their correlation is 0.6. The best fit line passes through the Y axis at Y = 40. Write the regression line. If a subject is 10 on x, what do you predict for Y? 2. You are looking through a book of new car ratings, and you decide to see how car weight influences EPA

### Regression Analysis

Sales and Advertising expenses are used for the simple regression data attached. Sales is the dependent variable and Advertising Expense is the independent variable. The results are on the spreadsheet, with the relevant items in boldface. In practical terms, - How do I convert the above information to an equation I can

### Various statistical problems

Please complete the following 5 questions in a Microsoft Word file: 1. When is it appropriate to use a time series approach to a business setting? When can it be applied to project management? 2. What are examples where control charts are used in your workplace to monitor quality control? What are the goals and objectives

(See attached file for full problem description with diagrams) --- 2. The following sample observations were randomly selected. Determine the coefficient of correlation and the coefficient of determination. Interpret. 3. Bi-lo Appliance Stores has outlets in several large metropolitan areas. The general sales manager

### Relating statistics to solve business related problems

Need guidance on how to approach a statistics paper. Problem: utilize statistics to solve a business related problem. The synopsis should include a discussion of the specific problem, the research methodology used, the quantitative and qualitative tools employed in the study, and the benefit and limitations of the research stud

### Regression to the mean

We expect that students who do well on the midterm exam in a course will usually also do well on the final exam. Gary Smith of Pomona College looked at the exam scores of all 346 students who took his statistics class over a 10-year period. *The least-squares line for predicting final exam score from midterm exam score was y

### Forecasting & Regression (12 Problems) : Hypothesis Testing and 2- and 3-Parameter Exponential Smoothing

On multiple choice, check any appropriate answers. For any hypothesis tests, use a .05 level of significance. Barrel temp. (in oF) Mold temp. (in oF) Injection pres. (in psi) Strength (psi) 320 100 7000 3877 320 100 5000 3901 320 100 3000 4010 320 125 7000 3706 320 125 5000 3708 320 125 3000 4140 320 150 7000

### Linear regression and Correlation

A. What is linear regression? b. What can linear regression do for you - both in a general business sense and specifically to your place of employment, or circle of influence? c. What are some of the limitations of regression analysis? a. What is correlation analysis and why is it important to us when we are using regres

### Regression and Hypothesis

The director of an investing group believes that the sales generated by a broker is related to the number of new clients a broker brings to the firm. The director believes that a linear regression model would help in the forecasting of expected sales from the brokers. To build the model the director takes a random sample of 12

### Regression Model with Advertising Expenditures and Price Index

Laura wanted to build a multiple regression model based on advertising expenditures and coffee times price index. Based on the selection of all normal values she obtained the following: 1) Multiple R = 0.738 2) R-square = 0.546 By using lagged values she came up with the following: 3) Multiple R = 0.755 4) R-square

### Regression

Find a bivariate data set relating to your work with a sample size of 10 or more. a) Determine which is the dependent variable Y and which is the independent variable X. Explain your reasoning. b) Draw a scatterplot and indicate the relationship if any. Explain. c) Compute the least-squares regression equation. Show majo

### Interpret the multiple regression analysis

EXHIBIT 1: A manager at a local bank analyzed the relationship between monthly salary and three independent variables: length of service (measured in months), gender (0=female, 1=male) and job type (0=clerical, 1=technical). The following tables summarizes the regression results: df

### 2 Sets of Problems - Regression : Correlation Coefficient, Scatterplot, Least-Squares and Data Analysis

(See attached file for full problem description) --- 47. In general, people tend to live longer in countries that have a greater supply of food. Listed below is the 1997 daily calorie supply and 2000 life expectancy at birth for 10 randomly selected countries. Country Calories (x) Life expectancy (y) Afghanistan 15