Suppose that you run a Regression Model and the Regression Coefficient for X has an associated p-value of 0.0070. At the 99% Confidence Level, is X significant? Enter a 1 into the blank below if X is significant and a O if it is not.
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- Suppose you are analyzing a dataset of 77 datapoints investigating the relationship between the number of theme park visitors (dependent variable) and temperature (independent variable). You decide to create a simple linear regression model, which yields a sum of squared residuals of e²=546.51. Using this information, what was the residual standard deviation (Se) found in your analysis? Note: 1- Only round your final answer. Round your final answer to two decimal places.The mean height of Americanwomen in their twenties is about 64.3 inches, and the standard deviation is about 3.9 inches. The mean height of menthe same age is about 69.9 inches, with standard deviationabout 3.1 inches. Suppose that the correlation between theheights of husbands and wives is about r 0.5.(a) What are the slope and intercept of the regressionline of the husband’s height on the wife’s height in youngcouples?(b) Draw a graph of this regression line for heights of wivesbetween 56 and 72 inches. Predict the height of the husbandof a woman who is 67 inches tall, and plot the wife’s heightand predicted husband’s height on your graph.(c) You don’t expect this prediction for a single couple to bevery accurate. Why not?If all participants in a repeated measures research study show roughly the same difference between treatments, then the data will produce a statistically significant value for the t statistic. True or false
- 10. Below is computer output from the least squares regression analysis on the body mass index, BMI, and percent body fat for 10 randomly selected adult males. Which of the following represents the 95% confidence interval for the slope of the regression line relating BMI and percent body fat for the population of adult males? Predictor Coef SE Coef P Constant -20.096 2.786 -7.213 0.000 BMI 1.695 0.2280 7.432 0.000 S = 3.195 R-Sq = 87.3% R-Sq(adj)=86.8% %3D (A) –20.096 ± 2.21(2.786) (B) 1.695 ± 7.432(0.2280) (C) 1.695 ±2.306(0.2280) (D) 1.695 ± 2.262(0.2280) (E) 1.695 ± 2.228(0.2280)The conditioned and unconditioned samples represent measurements taken from the same group of people at Conduct a paired t-test. different times or under different circumstances (e.g., before and after some conditioning program). The conditioned and unconditioned samples are made up of different v Choose... groups of people (e.g., athletes and non- Conduct an unpaired t-test. athletes). Conduct a regression analysis. See if the standard errors overlap. Conduct a paired t-test.The table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 90% confidence level with a redshift of 0.0126. Redshift Distance a. Find the explained variation. 0.0237 0.34 (Round to six decimal places as needed.) 0.0541 0.75 0.0723 0.98 C 0.0397 0.57 0.0444 0.62 0.0103 0.13
- The table below lists measured amounts of redshift and the distances (billions of light-years) to randomly selected astronomical objects. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 90% confidence level with a redshift of 0.0126. D Redshift Distance 0.0237 0.32 0.0545 0.75 0.0724 1.02 0.0397 0.56 0.0442 0.61 0.0103 0.16 a. Find the explained variation. (Round to six decimal places as needed.) b. Find the unexplained variation. (Round to six decimal places as needed.) c. Find the indicated prediction interval. billion light-yearsA university is concerned about the presence of grade inflation, which is defined as an increase in the average GPA of the institutions students over time without a comparable increase in academic standards. To investigate this phenomenon, the average GPA for the student body was recorded over the past 11 years, as shown in the table below. Construct a 95% confidence interval for the regression slope. Construct a 95% confidence interval for the slope. LCL= UCL= YEAR GPA 1 3.07 2 2.89 3 2.97 4 2.86 5 2.99 6 3.02 7 3.09 8 3.01 9 3.18 10 3.15 11 3.14Come up with your own data for a problem that would ask for a 90% confidence interval utilizing Z. Clearly label your variables.Researchers are examining the relationship between hours of sleep and athletic performance among college athletes. Athletic performance will be measured on a numeric scale, with greater numbers indicating better performance. The researchers expect that the more hours the athletes sleep, the better they will perform. Assuming all conditions for inference are met, the researchers will create a 95 percent confidence interval for the slope of the regression line for predicting athletic performance from amount of sleep. For which of the following would the confidence interval support the researchers' expectations? (A) The confidence interval includes only positive values. (B) The confidence interval includes only negative values. (C) The confidence interval has a width less than 1. (D) The confidence interval has a width greater than 1. (E) The confidence interval includes the value 0.Import the data from the Hill City Excel file into Minitab.You are trying to predict Price.Note: Mtn View=1 if there is a mountain view, 0 otherwise. The rest of the variables should be self explanatory. 1.Perform an F Test for overall significance of the model 2.Perform a T test for slope for the age variable 3. Find a 95% confidence interval for the slope of the SqFeet variable4. Create a prediction, CI, and PI for 1 new set of x values (any valid numbers you want), and interpret each.5. Run the residual plots and indicate if they show any problems with the model.The following output is from a multiple regression analysis that was run on the variables FEARDTH (fear of death) IMPORTRE (importance of religion), AVOIDDTH (avoidance of death), LAS (meaning in life), and MATRLSM (materialistic attitudes). In the regression analysis, FEARDTH is the criterion variable (Y) and IMPORTRE,AVOIDDTH, LAS, and MATRLSM are the predictors (Xs). The SPSS output is provided below, followed by a number of questions. Descriptive Statistics Mean Std. Deviation N feardth 27.0798 8.08365 163 importre 5.8282 2.46104 163 avoiddth 18.5460 6.97633 163 Las 70.1288 9.89460 163 matrlsm 53.5552 10.21860 163 Model Variables Entered Variables Removed Method 1 matrlsm, avoiddth, importre, lasa . Enter a. All requested variables entered. b. Dependent Variable: feardth Model Summary Model R R Square Adjusted R Square…SEE MORE QUESTIONS