Concept explainers
a.
1) Explain in what the point unusual was and whether it has high leverage, a large residual or both.
2) Explain whether the point is an influential point.
3) Explain whether removing the point result in a stronger or weaker
4) Explain whether the point were removed, would the slope of the regression line increase or decrease.
b.
1) Explain in what was the point unusual and whether it have high leverage, a large residual or both.
2) Explain whether the point is an influential point.
3) Explain whether removing the point result in a stronger or weaker correlation.
4) Explain whether the point were removed, would the slope of the regression line increase or decrease.
c.
1) Explain in what the point unusual was and whether it has high leverage, a large residual or both.
2) Explain whether the point is an influential point.
3) Explain whether removing the point result in a stronger or weaker correlation.
4) Explain whether the point were removed, would the slope of the regression line increase or decrease.
d.
1) Explain in what the point unusual was and whether it has high leverage, a large residual or both.
2) Explain whether the point is an influential point.
3) Explain whether removing the point result in a stronger or weaker correlation.
4) Explain whether the point were removed, would the slope of the regression line increase or decrease.

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Chapter 8 Solutions
STATS:DATA+MODELS-MY LAB ACC >CUSTOM<
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- Variable Temperature (X) Coffees sold (Y) Mean 35.08 29,913 Standard Deviation 16.29 12,174 Correlation -0.741arrow_forward13 A golf analyst measures the total score and number of putts hit for 100 rounds of golf an amateur plays; you can see the summary of statistics in the following table. (See the figure in Question 3 for a scatterplot of this data.)noitoloqpics bella a. Is it reasonable to use a line to fit this data? Explain. 101 250 b. Find the equation of the best fitting 15er regression line. ad aufstuess som 'moob Y lo esulav in X ni ognado a tad Variable on Mean Standard Correlation 92 Deviation Total score (Y) 93.900 7.717 0.896 Putts hit (X) 35.780 4.554 totenololbenq axlam riso voy X to asulisy datdw gribol anil er 08,080.0 zl noitsism.A How atharrow_forwardVariable Bone loss (Y) Age (X) Mean 35.008. 67.992 Standard Deviation 7.684 10.673 Correlation 0.574arrow_forward
- 50 Bone Loss 30 40 20 Scatterplot of Bone Loss vs. Age . [902) 10 50 60 70 80 90 Age a sub adi u xinq (20) E 4 adw I- nyd med ivia .0 What does a scatterplot that shows no linear relationship between X and Y look like?arrow_forwardVariable Temperature (X) Coffees sold (Y) Mean 35.08 29,913 Standard Deviation 16.29 12,174 Correlation -0.741arrow_forward2 Find and interpret the value of r² for the rainfall versus corn data, using the table from Question 14.2291992 b sgen gnome vixists 992 ms up? 2910 1999 bio .blos estos $22 tolqis2 qs rieds ni zoti swoH iisqa vilsen od 1'meo DOV to mogers boangas mus jil Reustar enou Leption20th ) abnuin Hagodt graub 032 Carrow_forward
- 18 Using the results from the rainfall versus corn production data in Question 14, answer oy the following: DOY 98 103 LA Find and interpret the slope in the con- text of this problem. b. Find the Y-intercept in the context of this problem. roy gatiigisve Toy c. Can the Y-intercept be interpreted here? (.ob o grinisq blo eiqmaxs as 101 galwollol edt 998 ds most notamotni er griau sib 952) siqmaxs steb godt llaw worl pun MAarrow_forwardVariable mean standard variation correlation temperature(X) 35.08 16.29. -0,741 coffees sold(Y). 29,913. 12.174.arrow_forward12 ம் Y si to no 1672 1 A medical researcher measures bone density and the age of 125 women; you can see the o lesummary of statistics in the following table. (See the figure in Question 2 for a scatterplot of this data.) a. How well will a line fit this data? b. Find the equation of the best fitting regression line. Variable Mean Standard Correlation Deviation Bone loss (Y) 35.008 7.684 0.574 A Age (X) 19 67.992 10.673 T in send art lo (d) sqala sala bolt 3 esmit sqola ad garrow_forward
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