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Chapter 1 : Drawing Statistical Conclusions

  1. Case Study 1.1: Explore the data, produce a histogram & boxplot and perform a t test (14:21)
    • In this video, it will show you how to:
      1. load the case 1.1 data into SPSS
      2. how to format the labels for the data
      3. recode the treatment so that it could be handled by the SPSS plotting and the t test programs
  2. ( There will be a demonstration on how to do a exploratory analysis of the data, plotting the data, calculating the means, box plot, student's t test )

  3. Case Study 1.2: Gender Discrimination (16:52)
    1. This case study is about the salary difference between the men and women in a corporation. This video will show you a different approach than the last Case Study 1.1 movie. You go on prometheus, download the data, analyze the data in data explorer to produce the histograms.
  4. Case Study 1.2 Stem Leaf Plot (09:08)
    1. In this video is a quick demostration to show you how to do a side by side stem leaf plot. By transfering the data from SPSS to Word Perfect and then using a Word Perfect table function to create a three column table and then entering the data in it.

Chapter 2: Inference using T-distributions

  1. Case Study 2.1:Sparrows (10:33)
    1. In this video, it will show you how to analyze the case study 2.1 data. This is data on sparrows that died in a storm outside of Brown University.
  2. Case Study 2.2: Schizophrenia (09:23)
    1. In this video, it will show you how to analyze the case study 2.2 data. (This is on Pg. 30, 2nd edition on Statistical Sleuth.) Its anatomical abnormalities are associated with schizophrenia and observational study. These are MRI measurements of the left hippocampus and fifteen sets of mono psychotic twins. Within the fifteen sets, one twin is affected by schizophrenia.

Chapter 3: A closer look at assumptions

  1. Case Study 3.1: Cloud Seeding (10:31)
    1. In this video, (Case study 3.1 Cloud Seeding to increase rainfall a randomized experiment.) First, you would load the data of the statistical sleuth data set. Then you will do an automatic recode; to code the treatment groups. After, you would number the cases to identify out wires. This video will also show you how to compute a logarithmic transform using the compute statement. (Also, other transforms with their compute statements.) Then perform a side by side box plot & independent sample T test.
  2. Case Study 3.2: Troops of Vietnam (17:59)
      • In this video, it will show you how to:
        1. load the data from the sleuth CD
        2. assign case numbers to the data
        3. side by side box plot matching display 3.3
        4. do 2 sample T test both with and without the out wires

Chapter 4: Alternatives to the t-tools

  1. Case Study 4.1: Space Shuttle O'ring failures (15:03)
    1. This video will cover case study 4.1 (Space Shuttle O'ring failures & observational study), where it will demonstrate how to analyze the data through Matlab.
  2. Case Study 4.2 (09:00)
    1. In this video will show case study 4.2 (Application of Wilcoxon's Rank Sum Test). There will be a set of data labeled cognitive load theory and teaching a randomized experiment. This video will guide you step by step to find evidence if there is a difference in the amount of time student spent solving a math problem with a conventional math approach versus the modified map approach.

Chapter 5: Comparisons among several samples

  1. Case Study 5.1: Diet restriction longevity (13:48)
    1. In this video, it will show you how to analyze the Case 5.1 data (Diet restriction longevity) a randomized experiment using the SPSS programs, One-Way, and UniANOVA. We will also analyze the contrasts which are described in the statistical summary (bottom of the p. 115 & 116).
  2. Case Study 5.1: Case 0501 ANOVA tables from summary statistics (13:42)
    1. In this video, it will give a demonstration on how to reconstruct an entire ANOVA table just based on the sample sizes, averages, and the standard deviations.
  3. Case Study 5.2: The Spock Conspiracy Trial (34:13)
    1. This will be a video on Case study 5.2 analysis of the percentage of women and the jury pools of the district court judges and especially an analysis of whether the percentage of women in the district court judge that oversaw the Spock trial in 1968 was different from the other juries.

Chapter 6: Linear combinations and multiple comparisons of means

  1. Case Study 6.1: Discrimination against handicapped (15:20)
    1. This will be an analysis of case study 6.1 (Discrimination against handicapped randomized experiment) The key variables for this case study is the score which are aptitude score assigned by students viewing movies of five different groups of actors for those groups involves different types of handicaps (control, amputee, crutches, hearing, & wheelchair); where it's been done with a recode (handled by value labels). There will be a demonstration to test the overall test of means among the five groups & test whether the amputee & hearing group is different from the crutches & wheelchair groups.
  2. Case Study 6.2: Preexisting preferences of fish (13:00)
    1. This is an analysis of case study 6.2 (Preexisting Preferences of fish). In this video, it will go over the basic outline of design. This is an experiment to test sexual selection by females. A.L. Basolo had the idea that female southern platy fish have a preference for sexual displays even in species which do not have tails designed for sexual displays. The experiment will have six pairs of males surgically given plastic sword tails but one of each of the 6 pairs were given a bright yellow tail and the other with a transparent tail.

Chapter 7: Simple linear regression: a model for the mean

  1. Case Study 7.1: The Big Bang - An observational study (12:21)
    1. This video will cover regression analysis using the case study 7.1 data (The Big Bang - An observational study). You will need to open this data with SPSS, data consist of resection velocity as the X variable & distance as the Y variable/ response variable. Also, it will show you how to do scatter plots for the data.
  2. Case Study 7.2: Meat processing and pH - a randomized experiment (9:50)
    1. This video will cover regression analysis using the case study 7.2 data (Meat procession & pH). This will show you how to calculate the predicted values for a given value of explanatory variable. This is a randomized experiment designed to come up with a regression equation to predict the pH of steers at different time after slaughtered.

Chapter 8: A closer look at assumptions for SLR

  1. Case Study 8.1: Island area and number of species (05:56)
    1. This video will analyze the case study 8.1 data (Island area & number of species). First load the data 8.1 and start off with a simple scatter plot of Island vs. Area; this will be matching the upper graph of display of 8.2. Further into this video, it will get into the interactive graph, regression line, and etc.
  2. Case Study 8.2: Breakdown time for insulating fluid (08:51)
    1. Case study 8.2 (Breakdown time for insulating fluid), you would first load the data from SPSS which consist the breakdown times for different voltages. Also, the data shows the natural log of time has already been computed. Then use the data to create an interactive graph. We have natural log of breakdown time as the dependent variable & the voltage as the independent variable.

Chapter 9: Multiple Regression

  1. Case Study 9.1: Effects of light on Meadowfoam (22:18)
    1. Case study 9.1 (Effects of light on Meadowfoam), first analyzing the data by using both regression & an identical approach with general linear model Univariate. For the linear regression, flowers would be the dependent variable/response variable & timing, intensity, and interaction would be the independent variables.
  2. Case Study 9.2: Large Brains (13:28)
    1. This video will cover case study 9.2 an observational study (Why do some mammals have large brains for their size). In this video, you would just work with the data which consist of variables: brain weight, body weight, gestation length, litter size. Also, consist of log transform variables: In (brain weight), In (body weight), In (litter size), & In (gestation length). Then create a scatter plot matrix to view each set of variables.

Chapter 10: Inferential tools for multiple regression

  1. Case Study 10.01: Galileo's data on parabolic motion (13:08)
    1. This video will be an analysis of case study 10.01 (Galileo's data on the motions of fallen bodies, a controlled experiment). This video will give a demonstration on how to analyze the following relationships of students interactive scatter plot where we have initial height as explanatory variable & horizontal distance (punti) as the response variable. The main questions to think about is: 1. in analyzing these data, is it adequate to use a linear fit or a smoother fit through the data? 2. can you explain it with a quadratic fit or does it require a cubic fit?
  2. Case Study 10.02 (34:15)
    1. The analysis of case study 10.02 (The energy cost of echo location by bats, an observational study), the questions to keep in mind is: 1. are the energy expenditures of echo locating bats greater than the non echo locating bats for the given body weight? 2. Are those expenditures different from that the non echo locating bats different from non echo locating birds?

Chapter 16: Repeated Measures

  1. Case Study 16.1: Sites of short-long term memory (27:04)
    1. This will be an analysis of case study 16.1 (Sites of short long-term memory, a controlled experiment, the book analyzes this data by doing two independent samples T test to see if there is a difference between short term memory between control and treatment groups & long term memory between control and treatment groups. Then after you see if they correlate with each other; you can do a multivariate test to find out either of those test are significant.
  2. Case Study 16.2: Oat Bran_Cholesterol (21:16)
    1. This will be an analysis of case study 16.2 (Oat Bran Cholesterol, a randomized crossover experiment), it will show you how to analyze these data using the general linear model repeated measures.