Run a regression analysis on the following data set, where yy is the final grade in a math class and xx is the average number of hours the student spent working on math each week. hours/weekx Gradey 4 51.6 5 43 13 82.2 14 90.6 15 89 16 80.4 16 89.4 18 97.2 19 91.6 19 97.6 State the regression equation with constants accurate to 2 decimal places.ˆyy^ = x + What is the predicted value for the final grade when a student spends an average of 11 hours each week on math? Round to 2 decimal places.

Run a regression analysis on the following data set, where yy is the final grade in a math class and xx is the average number of hours the student spent working on math each week. hours/weekx Gradey 4 51.6 5 43 13 82.2 14 90.6 15 89 16 80.4 16 89.4 18 97.2 19 91.6 19 97.6 State the regression equation with constants accurate to 2 decimal places.ˆyy^ = x + What is the predicted value for the final grade when a student spends an average of 11 hours each week on math? Round to 2 decimal places.

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 1TI: Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use...

See similar textbooks

hours/week x	Grade y
4	51.6
5	43
13	82.2
14	90.6
15	89
16	80.4
16	89.4
18	97.2
19	91.6
19	97.6

Similar questions

For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
The following calculation have been made for the price of 12 stocks(x) on NSEo on a certain day along with volume of sales on share (y) . From these calculations.calculatep the regression equation of price of stocks on volume of shares sigma X=580, sigma Y= 370, sigma XY=11494, sigma X square=41658 Sigma Y square = 17206 ?
For the population of Jellystone bears, the mean weight is 400 pounds and the mean blood pressure is 150, and the SLR regression slope for predicting blood pressure using weight has been found to be 0.05. What is the SLR regression intercept for predicting blood pressure using weight?
Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. What is the slope of the population regression line?
At a .01 level of significance is there sufficient evidence to conclude that the number of books sold is related to the number of registered students in a straight-line manner, and give the regression equation.
which of the following regressions represents the strongest negative linear relationship between x and y? (Attached in picture provided)
If the coefficient of determination for a simple regression is 38.9534%, what is the correlation value between the dependent and independent variable? Assume that the variables have a positive relationship. Enter your answer as a decimal.
A group of scientists are interested in finding out whether the days of rainfall during the dry season could predict the magnitude of butterfly migration in the local area during wet season. Every year for twenty years, they count the total days of rainfall during the dry season preceding the migration and measure the number of butterflies migrating in the following wet season. Which type of statistical test should they use to analyze their data? (pearson's r correlation OR, simple regression equation)
The following results are from data concerning the amount withdrawn from an ATM machine based on the amount of time spent at the ATM machine (SECONDS) and the gender, FEMALE (dummy variable = 1 for females and = 0 for males) and an interaction term, SECONDS*FEMALE Based on the regression results, what is the predicted amount withdrawn for a male who spends 20 at the ATM machine? (please express your answer using 1 decimal places)
You spilled water on your calculations from (a) and can't remember what your estimated regression parameters are. But you do have two possible estimated errors for each of your initial four observations:
Based on the results of the regression, what would be the mose likely conclusion?