![]() The output should include the P-value for the test and a 95% confidence interval for the mean. Choose the Minitab command "1-Sample Z" and enter the standard deviation which we will assume here to be known to be s = 7. In Minitab, look under "Stat" and then "Basic Statistics". Perform a statistical test of H 0 : μ = 41 versus H 1 : μ = 41 using a 5% level of significance. Does the Boxplot support your result? Does the Boxplot indicate the presence of any outliers? (Regardless of the results in this question, do the following questions as if the data is Normally distributed.) 4. Using both the P-value and the probability plot obtained in (2), explain at the 5% level of significance whether the null hypothesis HO: "data comes from a Normal distribution" should be retained or should be rejected in favour of the alternative hypothesis HA: "data comes from a non-Normal distribution". ![]() " and add "Mean symbol" to the current selections. Also, draw a Boxplot starting with "Graph", selecting "Boxplot", then "Simple". "Select" your column of data, and from "Distribution" choose "Normal". Start with the menu "Graph", and choose "Probability Plot" and then "Single". Check the sample for Normality and outliers as follows. Obtain summary quantities for your data by using "Stat", "Basic Statistics", "Display Descriptive Statistics" and "Selecting" your column of data. Input the data into a Minitab column and label it as "Ages". For this assignment, the data given below will be assumed to be a simple random sample. Use the P-value approach in making your Statistical Decisions since Minitab gives the P-value with each output requested below. Your solution should include all of your MINITAB output plus written answers to questions asked. this time with the response as weight and the predictor as height*.Use MINITAB to do the following problems. Now, it's just a matter of asking Minitab to performing another regression analysis. When you select OK, Minitab will enter the newly calculated data in the column labeled height*: Use the calculator that appears in the pop-up window to tell Minitab to make the desired calculation: First, label an empty column, C3, say height*: We can do that using Minitab's calculator. Now, using the fact that the mean height is 69.3 inches, we need to calculate a new variable called, say, height* that equals height minus 69.3. When you select OK, Minitab will display the results in the Session window: Then, select Mean, tell Minitab that the Input variable is height: The easiest way is to ask Minitab to calculate column statistics on the data in the height column. We can first ask Minitab to calculate \(\bar\) the mean height of the 10 students. It's easy enough to get Minitab to estimate the regression equation of the form: Now, as mentioned earlier, Minitab, by default, estimates the regression equation of the form: (The above output just shows part of the analysis, with the portion pertaining to the estimated regression line highlighted in bold and blue.) You may have to page up in the Session window to see all of the analysis. In our case, we again select weight as the response, and height as the predictor: In the pop-up window that appears, again tell Minitab which variable is the Response (Y) and which variable is the Predictor (X). Select Stat > Regression > Regression., as illustrated here: You can find regression, again, under the Stat menu. The second method involves asking Minitab to perform a regression analysis. A new graphics window should appear containing not only an equation, but also a graph, of the estimated regression line: In our case, we select weight as the response, and height as the predictor: In the pop-up window that appears, tell Minitab which variable is the Response (Y) and which variable is the Predictor (X). Select Stat > Regression > Fitted Line Plot., as illustrated here: You can find the fitted line plot under the Stat menu. Now, the first method involves asking Minitab to create a fitted line plot. In either case, we first need to enter the data into two columns, as follows: ![]() Let's use the height and weight example from the last page to illustrate. There are (at least) two ways that we can ask Minitab to calculate a least squares regression line for us.
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