10_W3_S2_LECTURE_NOTES

# 10_W3_S2_LECTURE_NOTES - Statistics 10 Week 3 Session 2...

This preview shows pages 1–8. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Statistics 10: Week 3, Session 2 Lecture Notes Professor Esfandiari Problem one: Given the following three plots and table, answer the questions posed. Y = Attitude toward seeking preventive care for breast cancer (scale 0—100) X = Knowledge of breast cancer (Scale of 0—100) ' r yx = 0.80 ° bl = 0.75 ° b0 = 15.95 a) What are the independent/predictor and dependent/outcome variables and are they qualitative or quantitative? b) What does r yx = 0.80 Show? 0) Given the above data, Terri concluded that acquiring more knowledge of breast cancer will lead to doing more mammograms? Is she right? Yes or no and why? (1) Write the least square regression line and interpret the slope and the intercept within context. e) Interpret R A2 f) What do you conclude from plot one with respect to regression assumptions? h) What do you conclude from plot two with respect to regression assumptions? i) What is the difference between plots 2 and 3 and what do they have in common? What does plot 3 show that plot 2 does not? j) Are there any leverage points? Why yes and why not? k) Are there any outliers? Why yes and why not? 1) If I remove the four point around which I have placed a circle in graph one, do you expect the coefficient of correlation to change and slope of the regression line to change or not and explain why? Plot one 100. 0 0 D 8 6 4 Emu «535553 mcEwam EmBS \$33; 100 SD 413 icncwiiedge of breast cancer Plot two 0 00 G 0 CO 03 C 000 0 00000000 0 O O 000000000000 00 0 0008000 00000 G 0 \$000 0000 OOOGO 000 W N GO 0 O O 0 00000006 G 00000 000 o o 00 000000 00 000 can 000000000000 0 000 a m o 00 com amoooeoc W 0 Go 0 06000000 0 mo W 9% a 0000 0000 ca 0m 0 m o a cocoon oao o o 0 00300 0 anoo U DO 0000 O 300 00 moo 00 o o w 0 o ‘ a m 0360 o G W o a oo 00 m o 0 a 0 o o 9 o o 0 W o ‘ ‘u .1 ““ 1 a r \‘M U C O C D 0 D D D 0 0 0 D WU 0 «Id U ﬂ 3 ﬂ 0 0 O wszmwm uwumnhmvcﬁmcb 1% m m H kﬁnudedge nfbfeastcancer Plot three 0 mmmmwmmmwwn 00 G O 00 00 0 000 0 90 00000000 0 O O GOﬁOOOOQOGOO 00 O Q 0000000 00000 O O O 0000 0000 QOOGU 000 O 0 00000000 0 00060 000 o o 00 000000 GO 000 0mm 000000000050 0 06\$ 0 o o 00 000 OOOODGQQ O 00 0 00006000 0 00 so 0 0000 0000 on 00 O 0 0 000000 000 o 0 m 0 00060 0 00003 W 0 00 0000 0 m 000 00 300 am 3 0 O 0 a G .0 0000 O .0 0 G ma GU O Qmu O D U D U 0. 3.00030 2 [300F113 1.0000er 0 m U U 1 333mg uwNmEmucﬁm «3.00000 £00 30 03 D £10 knowﬂedge ofbreastcancer Problem two: The following scatterplot has resulted from the Gestation data reported on problem 25 page 249 of chapter nine. Ch09_Gestation.txt Scatter Plot 700 600 aye 01 O O Geetatian d 00 O O 0 20 4O 60 80 Life_Expectancy_yrs Gestation = length of time from conception to birth All of the points in the above scatterplot represent mammals. The point with life expectancy = 80 belongs to a human. Question to be answered? Is the point related to humans, inﬂuential or not? What would happen to coefficient of correlation and slope if we remove this point? Problem three: Plot the following data. Y X 20.00 20 .00 20 .00 20 .00 21 .00 25 .00 25 .00 30.00 28.00 15.00 28 .00 16.00 25 .00 32.00 25.00 29.00 32.00 20.00 34.00 20.00 35.00 20 .00 35 .00 30.00 35 .00 10.00 40.00 18 .00 38 .00 22.00 35.00 30.00 10.00 70.00 a) What would happen to correlation and slope if we remove (10,70) and why? N0 calculations are necessary. b) Do you consider (10,70) to be leverage or outlier or both and why? Problem 4: Interpret the following plot. Jerry was hired to work on this data and he reported the coefficient of correlation between years since 1950 and rate (%) to be 0.70. If you were looking for somebody to work on your data, would you hire him? Explain why yes or why not. Ch09_lntrest_Rates1950-2005.txt Scatter Plot‘ H1950 1960 1970 1980 1990 2000 2010 Year ...
View Full Document

{[ snackBarMessage ]}

### Page1 / 8

10_W3_S2_LECTURE_NOTES - Statistics 10 Week 3 Session 2...

This preview shows document pages 1 - 8. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online