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 data set that does along with the below questions:

a. Description/Point Estimates: Central Tendency: write out the simple linear regression equation; specify and interpret bo and b1, using the units of the variables.

b. Description/Point Estimates: Dispersion: identify and interpret the standard error s, using the units of the variables.

c. Description/Point Estimates: r squared: identify and interpret the r squared, using the units of the variables.

d. Description/Point Estimates: r: identify and interpret r.

e. Inferences about the population regression coefficient: hypothesis test: specify the null and alternative hypotheses about the population regression coefficient; identify the p-value for the population regression coefficient, and use it to evaluate the Ho and Ha, include your interpretation, using the variables of the case.

f. Inferences about the population regression coefficient: confidence interval: specify and interpret the 95% confidence interval for the population regression coefficient, using the units of the variables.

g. Inferences about the predicted value of the dependent variable, using a given value of the independent variable: take as your given x value the mean of the x values: specify and interpret the 95% confidence interval and the 95% prediction interval, using the units of the variables. Be sure to clearly distinguish in your interpretation the difference between the 95% confidence interval and the 95% prediction interval, for the given value of x.

*Please note, the data set is attached. I copied and pasted a chart and put it in a word document. Thank you in advance for your assistance!

Simple linear regression

I. The Director of the Work/Study Program wanted to develop a model to predict a student’s grade point average, GPA, in relation to the number of hours worked. Choose a random sample of 20 students who work and are full-time students from our population in order to study the relationship between number of hours worked and gpa.

A. Plot a scatter diagram and assuming a linear relationship, use the least squared method to find the regression coefficients, b0 and b1.
B. Interpret the meaning of the regression coefficients IN THIS PROBLEM.
C.Determine the standard error of measurement.
D. Use the regression model developed in (A) to predict the gpa for a student who works 28 hours weekly.
E. Determine the coefficient of determination and interpret its meaning IN THIS PROBLEM.
F. Perform a residual analysis on your results and determine the adequacy of the model.
G. Would the model be appropriate for a student who worked 45 hours weekly?

Simple linear regression

The attached file contains observations of the American minimum wage during each of the years from 1950 through 1994. Has the minimum wage been growing at roughly a constant rate over this period? Use simple linear regression analysis to address this question. Explain your results.

Simple linear regression

A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:

(see table in attached file)

Referring to the table if the price of the candy bar is set at $2, the predicted sales will be

a. 30
b. 65
c. 90
d. 100

simple linear regression

A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:

(see table in attached file)

Referring to the table what is the estimated slope parameter for the candy bar and sales data?

A. 161.386
B. 0.784
C. -3.810
D. -48.193