Statistical Analysis of Survey Data - Prof. Adam Petrie, Quizzes of Statistics

Download Statistical Analysis of Survey Data - Prof. Adam Petrie and more Quizzes Statistics in PDF only on Docsity! Caitlin Farmer 000293787 Statistics 201 section 003 Project 1 1) My student ID ends in 87, so my random sample size is 387. 0 10 20 30 40 50 100.0% 99.5% 97.5% 90.0% 75.0% 50.0% 25.0% 10.0% 2.5% 0.5% 0.0% maximum quartile median quartile minimum 50 50 30.3 16 12 8 5 3 2 0.94 0 Quantiles Mean Std Dev Std Err Mean Upper 95% Mean Lower 95% Mean N 9.4914729 6.9377995 0.3526682 10.184864 8.7980818 387 Moments Q29 Most Miles Walked/Run One Day Distributions 2) I chose to analyze political beliefs, Q09. The charts show that the majority of people in statistics 201 consider themselves to be more conservative. However, there is not much of a difference between those with a mixture of liberal and conservative ideologies and those who are more conservative (the latter being only slightly less than 8% more than the former). Those who consider themselves more liberal are significantly outnumbered, a not so surprising finding in this part of the country. 0 100 17.8% 45.0% 37.2% Q09 Political Beliefs Chart 17.8% 45.0% 37.2% Q09 Political Beliefs Chart Q09 Political Beliefs More Liberal More Conservative A Mix of Liberal And Conservative Caitlin Farmer 000293787 Statistics 201 section 003 Project 1 3) One of the most notable differences between male and female favorite alcoholic drinks is the male preference for beer is significantly more than the female preference for beer (or preference for any other drink for that matter). Another notable difference is that while the answer “I don’t drink” is the number one choice for females, the number of females who don’t drink is not significantly more than the number of males who don’t drink. This is because the choices for females are much more evenly distributed than the choices for males. For females, the choices of not drinking, vodka, wine, and beer are all fairly similar while for males, beer is the clear choice. 0 20 40 60 80 100 120 140 160 180 Q39 Favorite Alcoholic Drink 0 10 20 30 40 50 60 70 80 90 100 Plots Pareto Plot Q02 Gender=M 0 50 100 150 200 Q39 Favorite Alcoholic Drink 0 10 20 30 40 50 60 70 80 90 100 Plots Pareto Plot Q02 Gender=F 4) I chose to analyze the number of Harry Potter books read, Q50. -2.33 -1.64 -1.28 -0.67 0.0 0.67 1.28 1.64 2.33 0.5 0.8 0.2 0.05 0.01 0.95 0.99 50 100 0 1 2 3 4 5 6 7 8 Normal(3.31008,3.10306) 100.0% 99.5% 97.5% 90.0% 75.0% 50.0% 25.0% 10.0% 2.5% 0.5% 0.0% maximum quartile median quartile minimum 7 7 7 7 7 3 0 0 0 0 0 Quantiles Mean Std Dev Std Err Mean Upper 95% Mean Lower 95% Mean N 3.3100775 3.1030616 0.1577375 3.6202097 2.9999453 387 Moments Location Dispersion Type μ σ Parameter 3.3100775 3.1030616 Estimate 2.9999453 2.8987629 Lower 95% 3.6202097 3.3385783 Upper 95% -2log(Likelihood) = 1973.72770024145 Parameter Estimates Shapiro-Wilk W Test 0.763731 W <.0001 * Prob<W Note: Ho = The data is from the Normal distribution. Small p-values reject Ho. Goodness-of-Fit Test Fitted Normal Q50 # Harry Potter Books Read a) The distribution is clearly bimodal, but relatively symmetric. There are no outliers or gaps in the data. Caitlin Farmer 000293787 Statistics 201 section 003 Project 1 b) No I Wish I Weighed Less No I Wish I Weighed More Yes 113 65 20 11 132 46 178 31 178 265 122 387 Q08 Frat/Sorority? Count No Yes Contingency Table 0.00 0.25 0.50 0.75 1.00 Q05 Satisfied With Weight? No Yes Mosaic Plot c) The contingency table and the mosaic plot do support the plausibility that there is a relationship between participation in Greek life and satisfaction with weight. There is a higher frequency of people who are not satisfied with their weight being a member of a sorority or fraternity than those who are satisfied. Being a member of a social Greek organization could put pressure on people to look a certain way and thus lower their comfort with their body size. 7) a) 2 3 4 100 200 Q22 Fastest Speed Driving MPH Bivariate Normal Ellipse P=0.950 Q22 Fastest Speed Driving MPH Q07 GPA Variable 102.6404 3.272913 Mean 17.81423 0.471622 Std Dev -0.25656 Correlation <.0001 * Signif. Prob 381 Number Correlation Bivariate Fit of Q07 GPA By Q22 Fastest Speed Driving MPH b) Caitlin Farmer 000293787 Statistics 201 section 003 Project 1 3 4 70 80 90 100 110 120 130 140 150 Q22 Fastest Speed Driving MPH Bivariate Normal Ellipse P=0.950 Q22 Fastest Speed Driving MPH Q07 GPA Variable 101.1616 3.318245 Mean 14.58616 0.416538 Std Dev -0.29817 Correlation <.0001 * Signif. Prob 359 Number Correlation Bivariate Fit of Q07 GPA By Q22 Fastest Speed Driving MPH c) There appears to be a correlation between GPA and fastest driving speed. 29.8% of the variation in driving speed can be accounted for by GPA. The assumptions for correlation, which are straight enough, no outliers, and quantitative data, hold up. d) 50 60 70 80 90 100 110 120 130 140 150 1.5 2 2.5 3 3.5 4 Q07 GPA Bivariate Normal Ellipse P=0.950 Q07 GPA Q22 Fastest Speed Driving MPH Variable 3.320595 98.10714 Mean 0.483759 13.75319 Std Dev -0.1415 Correlation 0.0673 Signif. Prob 168 Number Correlation Bivariate Fit of Q22 Fastest Speed Driving MPH By Q07 GPA Q42 Used Illegal/Controlled Substance?=No 70 80 90 100 110 120 130 140 2 2.5 3 3.5 4 Q07 GPA Bivariate Normal Ellipse P=0.950 Q07 GPA Q22 Fastest Speed Driving MPH Variable 3.249487 103.6769 Mean 0.447158 15.03718 Std Dev -0.25241 Correlation 0.0004 * Signif. Prob 195 Number Correlation Bivariate Fit of Q22 Fastest Speed Driving MPH By Q07 GPA Q42 Used Illegal/Controlled Substance?=Yes The strongest correlation is by people who have used a controlled substance (-.252) and the weakest is by people who have not (-.142). This does support recklessness as being a lurking variable because people who have used an illegal substance can be considered more reckless. 8) a) Caitlin Farmer 000293787 Statistics 201 section 003 Project 1 55 60 65 70 75 80 100 200 Q22 Fastest Speed Driving MPH Linear Fit Q03 Height = 61.773696 + 0.058059*Q22 Fastest Speed Driving MPH RSquare RSquare Adj Root Mean Square Error Mean of Response Observations (or Sum Wgts) 0.059227 0.056778 4.136722 67.7294 386 Summary of Fit -15 -5 5 15 55 60 65 70 75 80 Q03 Height Predicted Residual by Predicted Plot Diagnostics Plots Linear Fit Bivariate Fit of Q03 Height By Q22 Fastest Speed Driving MPH The least squares regression line equation is Q03 Height=61.77+0.06*Q22 Fastest Speed Driving. The slope, 0.06, means that for every additional mile per hour, height increases by 0.06 on average. The residual plot shows no pattern and is approximately normal. The assumptions for using regression, straight enough, quantitative data, and no outliers, are met. b)R squared= 0.059. This means that 5.9% of the variation in height can be explained by fastest speed driving. c)