Mathematics department at a liberal arts college used a 25-point placement test to assist in assigning
Problem: Mathematics department at a liberal arts college used a 25-point placement test to assist in assigning appropriate math courses to incoming freshmen. The department believes that the test is a good predictor of a student’s final numerical grade in its introductory statistical course. The results are shown below.
| Student | Placement Test Score ( x ) | Numerical Grade ( y ) |
| 1 | 21 | 69 |
| 2 | 17 | 72 |
| 3 | 21 | 94 |
| 4 | 11 | 61 |
| 5 | 15 | 62 |
| 6 | 19 | 80 |
| 7 | 15 | 65 |
| 8 | 23 | 88 |
| 9 | 13 | 54 |
| 10 | 19 | 75 |
| 11 | 16 | 80 |
| 12 | 25 | 93 |
| 13 | 8 | 55 |
| 14 | 14 | 60 |
| 15 | 17 | 64 |
- Construct a scatter plot for the data.
- Find the least square line relating x to y .
- Plot the least square line on the graph, part (a).
- Interpret the values of \[{{\overset{\scriptscriptstyle\frown}{\beta }}_{0}}\] and \[{{\overset{\scriptscriptstyle\frown}{\beta }}_{1}}\] .
Suppose that you obtained the following summary quantities to estimate the parameters in a regression study. Assume that x and y are related according to the simple linear regression model: \[\hat{y}\] = \[{{\hat{\beta }}_{0}}\] + \[{{\hat{\beta }}_{1}}\] x .
n = 14, \[\sum\limits_{i=1}^{n}{{{y}_{i}}}\] = 572, \[\sum\limits_{i=1}^{n}{y_{i}^{2}}\] = 23,530, \[\sum\limits_{i=1}^{n}{{{x}_{i}}}\] = 43, \[\sum\limits_{i=1}^{n}{x_{i}^{2}}\] = 157.42, and \[\sum\limits_{i=1}^{n}{{{x}_{i}}}{{y}_{i}}\] = 1697.80.
- Calculate the least square estimates of the slope ( \[{{\hat{\beta }}_{1}}\] ) and intercept ( \[{{\hat{\beta }}_{0}}\] ).
- Estimate the variance of the error term, 2 .
Problem: Use Minitab to solve this problem. To examine the relationship between the size (in square feet) of a store and its annual sales (in thousands of Dollars), a sample of 14 stores was selected. The results for those 14 stores are summarized in the table below.
| Store | Size ( x ) | Sales ( y ) |
| 1 | 1,726 | 3,681 |
| 2 | 1,642 | 3,895 |
| 3 | 2,816 | 6,653 |
| 4 | 5,555 | 9,543 |
| 5 | 1,292 | 3,418 |
| 6 | 2,208 | 5,563 |
| 7 | 1,313 | 3,660 |
| 8 | 1,102 | 2,694 |
| 9 | 3,151 | 5,468 |
| 10 | 1,516 | 2,898 |
| 11 | 5,161 | 10,674 |
| 12 | 4,567 | 7,585 |
| 13 | 5,841 | 11,760 |
| 14 | 3,008 | 4,085 |
- Fit the least squares line to the data to write the simple linear regression equation.
- Plot the data and graph the least squares line as a check on your calculations.
- Is the predictor "Size" significant at =0.05?
- Interpret the meaning of the slope parameter 1 .
- Evaluate the assumptions (check on is i.i.d. N(0, 2 )).
- What are the values of SSE and s 2 ? What does " s " mean?
- What is the 95% Confidence Interval for the slope?
Deliverable: Word Document
