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# Regression Analysis

### Regression / Difference Testing (ECONOMETRICS)

Suppose you have cross-section data on income (y) and electricity consumption (x) for three regions and you have regressed ln x on an intercept and ln y for each region and for the full sample, obtaining the following results: Estimated Intercept Std Error. Slope Std. Error SSE n Region A 0.02 0.008 1.10 0.05 48 92 Region

### SPSS- Correlation Assignments

ASAP!! Hi Martin! I have updated this assignment with the correct data set. I also sent you a message requesting that the first assignment be changed with this data set as well. I did not realize I had sent you the incorrect data set. I am so sorry. I have been working too much, and trying to do way too much. Again, I apo

### SPSS-Correlation Coefficient & Regression Analysis

Hello Martin! Here is the next group of info for your help, and expertise. If I may ask, please do me a favor and report the results in two seperate docs so I can keep them straight. The first doc in SPSS, a determination of whether there is a correclation between age, and height. I will send the original doc way back from my

### Correlation: Compute a correlation matrix that includes all continuous variables. Identify all individual correlations that are significant at the 95 percent level. Regression: Build a multiple regression model to explain the variability in the median school year. Describe the goodness of fit of your model and summarize your findings. Select at least four to seven similar independent variables from the remaining forty-nine measures and justify your selection.

I need help in conducting correlation and regression analyses using the provided SampleDataSet.xlsx. Correlation: Compute a correlation matrix that includes all continuous variables. Identify all individual correlations that are significant at the 95 percent level. Regression: Build a multiple regression model to explain t

### Time series forecast

The U.S. Census Bureau publishes data on factory orders for all manufacturing, durable goods, and nondurable goods industries. Shown here are factory orders in the United States from 1987 through 1999 (\$ billion). (a) Use these data to develop forecasts for the years 1992 through 2000 using a 4-year moving average. (b) Use the

### Predicting GPA from ACT Scores using the Regression Equation

1. A government survey conducted to estimate the mean price of houses in a metropolitan area is designed to have a margin of error of \$10,000. Pilot studies suggest that the population standard deviation is \$70,000. Estimate the minimum sample size needed to estimate the population mean with the stated accuracy. 2. You a

### Forecasting 5th Year Student Enrollment

Notice the 5th year student enrollment forecast. Why are the 5th year forecast of numbers linear in comparison to the previous 4 years noted in the line graph indice and calculations? Why are the numbers in 5th year gradually increasing each month starting from January, but previous years, month after month, are scattered?

### Correlation Analysis

Run either correlation, regression, or discriminant analysis on your chosen data. This Application requires you to engage in data interpretation and to select the appropriate analyses for your hypotheses and for the data that you have at your disposal. Toward that end, you should consider which analyses will inform the reader an

### Correlation analysis and Pearson Correlations

Student Admissions Admissions want to investigate alternate means of determining who will be admitted to their university as freshmen. The admissions counselors determine that one of the best predictors of undergraduate grade point average (GPA) is a student's high school GPA. They compile information through their databases ab

### Prediction with linear and quadratic regression line

Problem 3 Do the problem and answer all the questions. Hint for question: Compare the errors on the basis of the Mean Absolute Deviation (MAD)? Copy and paste the following data set in Excel to save the typing effort and possible mistakes in data entry. Year Shipments 1985 14,705 1986 15,064 1987 16,676 1988 18,061

### Four key marketing decision variables are price (P), advertising, (A), transportation (T), and prodict quality (Q). Consumer demand (D) is influenced by these variables. The simplest model for describing demand in terms of these variables is D=k-pP+aA+tT+qQ where k, p, a, t and q are constants. Discuss the assumptions of this model. Specifically how does each variable affect demand> How do the variables influence each others? What limitations might this model have? How can it be improved?

Four key marketing decision variables are price (P), advertising, (A), transportation (T), and prodict quality (Q). Consumer demand (D) is influenced by these variables. The simplest model for describing demand in terms of these variables is D=k-pP+aA+tT+qQ where k, p, a, t and q are constants. Discuss the assumptions of this mo

### Simple Linear Regression

I am having a difficult time understanding this concept as a whole. I can explain it in writing, but when it comes to calculations, it is very difficult. In the following case, I need to review the outputs for the service time data relates to predicting services times for 1, 2, 3, 4, 5, 6, and 7 copiers. I have attached the d

### Multiple Regression Analysis: Statistical Significance

A shoe manufacturer is considering the development of a new brand of running shoes. The business problem facing the marketing analyst is to determine which variables should be used to predict durability (i.e. the effect of long term impact). Two independent variables under consideration are X1 (FOREIMP), a measurement of the f

### Simple Linear Regression: Example Problem

The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The data attached represents the value in 2009 (in millions of dollars) and the annual revenue (in millions of dollars) and the annual revenue (in millions of dollars) for the 30 major league baseball franchises. Suppose

### Regression analysis in SPSS

a) Means, sums of squares and cross products, standard deviations, and the correlation between X and Y. b) Regression equation of Y on X. c) Regression and residual sum of squares. d) F ratio for the test of significance of the regression of Y on X, using the sums of squares (i.e., SSreg and SSres) and r_xy^2. e) Variance o

### Developing Cost Function Estimates for the UPS Store Franchise

Develop cost function estimates for "The UPS Store" franchise opportunity. Use the methods outlined in the attached file: account classification, high-low, and regression analysis methods. How valid are the cost function estimates that you generated? Which method provided the best estimate? Why? Use data from franchise op

### Help with Statistics Questions

Brian, this is Bernice. I have three questions that I hope you can help me with. I have retired from WCCCD, so I am using my personal email address. I hope you can help. 1. What is simple linear regression, and why is it useful? How would we predict a raw score from a raw score? What is the difference between the intercept an

### Three Time Series Analysis Questions

Question 1 Suppose E(X) = 2, Var(X) = 9, E(Y)=0, Var(Y)=4 and Corr(X,Y)=0.25. Find: a) Var(X + Y) b) Cov(X, X + Y) c) Corr(X + Y, X - Y) Question 2 If X and Y are dependent but Var(X) = Var(Y), find Cov(X + Y, X - Y) Question 3 Suppose Yt = 5 + 2t +Xt, where (Xt) is a zero-mean stationary series with autoco-varianc

### Develop a regression model to predict the price of two stocks

The closing stock price for each of two stocks was recorded over a 12-month period. The closing price for the Dow Jones Industrial Average (DJIA) was also recorded over this same time period. These values are shown in the following table: DJIA Stock 1 Stock 2 11,168 48.5 32.4 11,150 48.2

### Regression Analysis on TV Viewing by Age

Researchers have collected data on the hours of television watched in a day and the age of a person. The data: Age Hours of Television 30 2 65 7 72 6 41 4 Additional Info: y= -.698 + .105x a) Find coefficient of det

### Simple Linear Regression, Multiple Regression Model, and CI

1. A simple linear regression model relating investment (y) by companies to bank lending interest rate (x) is stated as error term the is where y=?o+?1x+? where is ? is the error term a. What are the intercept and slope for the relationship between investment and interest rate stated above? What sign would you expect for th

### Multiple Regression Analysis, Time Series Analysis

See attached data file. Background: One day, after reporting the performance of the company to the shareholders, the CEO of A. Fictitious & Co. decided that he would like to quantify the impact of the company's expenditures has on how much sales it generates. In other words, he would like to know if the company increases t

### Develop a Linear Regression to Predict MPG

A sample of twenty automobiles was taken, and the miles per gallon (MPG), horsepower and total weight were recorded. Develop a linear regression model to predict MPG using horsepower as the only independent variable. MPG HORSEPOWER WEIGHT 44 67 1,844 44 50 1,998 40 62 1,752 37 69 1,980 37 66 1,797 34 63 2,199 35 90 2,

### Linear regression model for hospital factors of number of beds, number of admissions, and total expenses

The total expenses of a hospital are related to many factors. Two of these factors are the number of beds in the hospital and the number of admissions. Data were collected on the 14 hospitals, as shown in the table: HOSPITAL NUMBER OF BED's ADMISSSIONS (100s) TOTAL EXPENSES (MILLIONS) 1 215 77 57 2 336 160 127 3 520 230 15

### Regression Analysis of Restaurant ratings In New York City and Long Island: Food, decor, service and price

See attached data file. Zagat's publishes restaurant ratings for various locations in the United States. The file RESTRATE.xls contains the Zagat rating for food, decor, service, and the price per person for a sample of 53 restaurants located in New York City and 53 restaurants located in Long Island. Suppose you wanted to de

### Using a Regression Model

Steve Caples, a real estate appraiser in Lake Charles Louisiana, has developed a regression model to help appraise residential housing in the Lake Charles Area. This model was developed using recently sold homes in a particular neighborhood. The price (Y) of the house is based on the square footage (X) of the house. The mode

### Correlation and Simple Linear Regression

Take your data and arrange it in the order you collected it. Count the total number of observations you have, and label this number N. Then create another set of data starting from one and increasing by one until you reach N. For example, if you have 10 observations, then your new set of data would be (1, 2, 3, 4, 5, 6, 7,

### Multiple Regression & Time Series Forecasting

The CEO noticed that he has five years of quarterly sales data in hand, and they form a time series. He decided to also ask you to perform time-series analysis on it, and use it to forecast what future sales are expected to be at the end of 1Q 2009. 1. Plot the quarterly sales as a function of time in your Excel data spreads

### statisitcs questions

6. Company officials are concerned about the length of time a particular drug retains its potency. A random sample (sample 1) of 10 bottles of the product is drawn from current production and analyzed for potency. A second sample (sample 2) is obtained, stored for one year, and then analyzed. The data is in file potency. Prob

### Correlation and Regression....

A researcher wishes to determine the relationship between the number of cows (in thousands) in countries in southwestern Pennsylvania and the milk production (in millions of pounds). The data are shown. Describe the relationship Cows x 70 3 194 12 46 65 Pounds y 115 5 289 15 72 92 (a) find the values of the correlatio