Larger engines use more gas. Here is data comparing the vehicle engine size in liters to city mile per gallon (mpg). Size 6.2 5.4 3.0 3.9 3.6 5.0 6.4 3.5 City mpg 13 14 20 19 19 20 15 17 Test the claim of correlation at the α = 0.05 level of significance. Find the P-value Will the null hypothesis be rejected? Round the regression equation to one decimal place and use it to predict the city mpg of a vehicle with an engine 6.0 L in size.

Larger engines use more gas. Here is data comparing the vehicle engine size in liters to city mile per gallon (mpg). Size 6.2 5.4 3.0 3.9 3.6 5.0 6.4 3.5 City mpg 13 14 20 19 19 20 15 17 Test the claim of correlation at the α = 0.05 level of significance. Find the P-value Will the null hypothesis be rejected? Round the regression equation to one decimal place and use it to predict the city mpg of a vehicle with an engine 6.0 L in size.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
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The average midterm score in a large statistics class was 70 with an SD of 10. The average final score in the same class was 65 with an SD of 15. The correlation coefficient between midterm and final scores was r=0.6. Using the regression line, we predict the final score of a student with a midterm score of 60 to be , but this prediction is likely to be off by about . Fill in the blanks, rounding each answer to one decimal point.
Using the regression line attached. Based on only the above plot, one can conclude: a) height causes an increase in weight b) weight causes an increase in height c) taller people are more likely to weigh more than shorter people, at least in the sample on which this data is based d) a statistically significant predictive relationship between height and weight e) c and d
A group of students measure the length and width of a random sample of beans. They are interested in investigating the relationship between the length and width. Their summary statistics are displayed in the table below. All units, if applicable, are millimeters. Mean width: 7.555 Stdev width: 0.914 Mean height: 12.686 Stdev height: 1.634 Correlation coefficient: 0.8203 d) If the students are interested in using the height of the beans to predict the width, calculate the slope of this new regression equation. e) Write the equation of the best-fit line that can be used to predict bean widths. Use x to represent height and y to represent width.
A marketing manager conducted a study to determine the relationship between money spent on advertising (X) and company sales (Y). The study consisted of 8 companies and the data is given below and is in units of $1000s (ie. 2.4 = $2400.00) d. What is the resulting residual value when advertising expenditure is $2200.00 (X = 2.2), that is the difference between the actual observed value of y and the predicted value of y when using the fitted regression equation? e. What percentage of the variation in company sales is explained by the regression equation? In other words, what is the variability in Y that is due to advertising? Does a…
Use the given data to find the scatterplot, equation of the regression line, and prediction. x 1.2 1.4 1.6 1.8 2.0 y 54 53 55 54 56 Let α=0.05 Scatterplot from the calculator: What is the linear regression equation? (Round to 3 decimal places) ^ y= _________________________________________________________________ r = _______________ critical value from table: ________________ Is there a strong enough linear correlation to use the regression equation given? ___________ Explain why? _____________________________________________________ What is the best predicted y-value when x=1.9 ? _______________
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A researcher wants to forecast the annual sales of Walmart, based on store size. To examine the relationship between the store size in square feet and its annual sales in million dollars, a sample of 14 stores was selected shown below in the image: Answer the following: i) Null hypothesis of correlation ii) Coefficient of Correlation and its interpretation iii) Interpret the sig value of ANOVA. iv) Coefficient of Determination and its interpretation v) Write down the Regression Model. vi) Interpret the value of ‘a’ vii) Interpret the value of ‘slope’
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Suppose the following data were collected relating the selling price of a house to square footage and whether or not the house is made out of brick. Use statistical software to find the regression equation. Is there enough evidence to support the claim that on average brick houses are more expensive than other types of houses at the 0.010.01 level of significance? If yes, type the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Price Sqft Brick (1 if brick, 0 if otherwise 241255 3392 0 184518 2038 1 176488 1906 0 240068 3329 0 169760 1828 0 185335 2081 0 172735 1926 0 224281 3425 0 172589 1676 1 214635 2735 1 199666 2373 1 208348 2662 1 218360 2834 1 230160 3254 0 164812 1431 0 191560 1839 1 203255 2456 1 173325 1530 0 168073 1381 1 179620 1457 1 Selecting a checkbox will replace the entered answer value(s) with the checkbox value.…