Elementary Statistics Using The Ti-83/84 Plus Calculator, Books A La Carte Edition (5th Edition)
5th Edition
ISBN: 9780134688886
Author: Mario F. Triola
Publisher: PEARSON
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Chapter 10.2, Problem 2BSC
To determine
The difference between the regression equation
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How use simple regression to explore the relationship between two variables?
Pls help with below homework-)
Show transformation of switching equation into a regression equation.
What is the relationship between the amount of time statistics students study per week and their final exam scores? The results of the survey are shown below.
Time
10
1
8
10
8
5
9
6
Score
87
58
72
75
77
71
85
78
The equation of the linear regression line is: y^ = + x (Please show your answers to two decimal places)
Use the model to predict the final exam score for a student who spends 9 hours per week studying.Final exam score = (Please round your answer to the nearest whole number.)
Chapter 10 Solutions
Elementary Statistics Using The Ti-83/84 Plus Calculator, Books A La Carte Edition (5th Edition)
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- What is regression analysis? Describe the process of performing regression analysis on a graphing utility.arrow_forwardIf your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?arrow_forwardhow do I check column to verify computations (e.g., ∑ (Y-Yc)=0) and interpret the meaning of regression coefficients.arrow_forward
- A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds), Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session These variables are described below: Y-BO+B1'X+82'D 83'X'D+E Y-Weight loss (in pounds) X- Length of time in weight-loss program (in months) D-1 if morning session. O if not in terms of the Bs in the model, what is the difference between the weight loss of an individual who has spent 3 months in the program when attending the morning session, and an individual who has spent 2 months in the program when attending the evening session? OB1+83 OB1+82-83 OB1+82+283 O81+82+383arrow_forwardHow do you calculate the coefficient of determination (R^2) in a simple linear regression model?arrow_forwardWhat is the slope and intercept for the regression equation given this data? X = 72, 81, 54, 50, -3 Y = 69, 97, 73, 22, -10 Please format to 2 decimal places.arrow_forward
- Suppose you do a simple regression were you predict the number of sales ofcold drinks ( y )using the variable temperature in degrees Celsius ( x )Also Suppose you obtain the regression output below coefficient standard error intercept 100 10 temperature 3 1 So the regression equation is y=100+3x What is the equation of the regression line Select one: a. number of sales=-100+2price-3temperature b. None of the choices are correct c. number of sales=100+3temperature d. Intercept=100-2price+3temperature e. 100+3= 103arrow_forwardPlease show me how to find the regression equation on excel for this data, when I try to do it it says ' Input range contains non-numeric data.'' please helparrow_forwardHow to write the model specifications of a multiple regression analysis with dummy variables that are more than two categories? Provide your solutions using the example, y=spending,x1=education level (1 if primary,2 if secondary,3 if tertiary,4 if above),x2=type of job(1 if full times,2 if part timed,3 if retired) Write the regression equation of the example.arrow_forward
- What are the functional forms of the regression model? Explain.arrow_forward· Develop a simple linear regression equation for starting salaries using an independent variable that has the closest relationship with the salaries. Explain how you chose this variable.arrow_forwardThe electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature (x1), the number of days in the month (x2), the average product purity (x3), and the tons of product produced (x4). A multiple linear regression analysis was applied to the experimental data using MINTAB and the following results were obtained: Regression Analysis: y versus x1, x2, x3, x4 Regression Equation y = -123 + 0.757 x1 + 7.52 x2 + 2.48 x3 - 0.481 x4 Coefficients Term Coef SE Coef T-Value P-Value VIF Constant -123 157 -0.78 0.459 x1 0.757 0.279 2.71 0.030 2.32 x2 7.52 4.01 1.87 0.103 2.16 x3 2.48 1.81 1.37 0.212 1.34 x4 -0.481 0.555 -0.87 0.415 1.01 Analysis of Variance Source DF Seq SS Seq MS F-Value P-Value Regression 4 5600.5 1400.1 10.08 0.005 Error 7 972.5 138.9 Total 11 6572.9 Then, the upper limit of the two-sided 95% confidence interval on the slope B2 is equal to а. 17 O b. 20 19arrow_forward
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