n educational psychologist is examining response times to an on-screen stimulus.  The researcher believes there might be a weak effect from age, but expects a more pronounced effect for different color contrasts.  She decides to examine a black on white (B/W) combination compared to 2 alternatives:  red on white (R/W) and yellow on blue (Y/B).  Here is the data for response times (in milliseconds):       B/W     R/W     Y/B       16–17   8 44 21 20 17 32 21 24 22 24 45 5 9 35 21 26 8 33 19 13 23 28 19 7   18–19   37 17 35 39 36 19 15 47 24 13 16 11 19 23 15 32 24 32 37 24 32 37 29 46   20–21   38 17 22 26 23 15 26 20 41 29 26 19 29 20 31 27 18 37 44 29 41 40 27 44 Using MS Excel, conduct a 2-way ANOVA with α=0.05α=0.05.  Fill in the summary table. The last column in the table below is read as, "Partial Eta-squared " and is a concept that was NOT covered in the lectures. Partial ηη 2 is used when there is a chance the data is not independent (click here to read more if interested) Computing the values for "Partial ηη 2" is not difficult. Simply divide SSeffect by the sum of SSeffect and SSerror. For example, Partial ηη 2(A) = SSA /(SSA + SSerror) P-values should be accurate to 4 decimal places and all other values accurate to 3 decimal places. Source SS df MS F-ratio P-value Partial η2η2 Age (AA)   2         Color (BB)   2         Interaction (A×B)(A×B)   4         Error   63

A random sample of college students was surveyed about how they spend their time each week.  The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week.  The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows:             Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to the…

A veterinarian collects data about puppies in her practice. She makes a scatterplot with their age in days on the horizontal axis and weight in ounces (oz) on the vertical axis and sees that the scatterplot is football shaped. The average age of the puppies is 42 day, with an SD of 7 days. The average weight is 25 oz, with an SD of 5 oz. The correlation is moderately strong, with r = 0.7. 4. (a) The age of two puppies differ by 20 days. The difference in their weight is predicted to be oz. (b) The baseline prediction for the weight of puppies ignores their age and just uses the information from all puppies. The baseline predicted weight is oz and the RMS error for this prediction is o. (c) Suppose we converted the units of age from days to weeks. The average age is now weeks, with an SD of weeks. The correlation between age in weeks and weight in oz is now r =

You are interested in the relationship between gender and violent victimization. You have the data below: Victimization No Yes Total Gender b. Interpret Lambda. Male 150 50 200 Female 190 10 200 Total 340 60 400 a. Calculate Lambda. If needed, take a picture of your work and insert directly into the workspace.

Ocean currents are important in studies of climate change, as well as ecology studies of dispersal of plankton. Drift bottles are used to study ocean currents in the Pacific near Hawaii, the Solomon Islands, New Guinea, and other islands. Let x represent the number of days to recovery of a drift bottle after release and y represent the distance from point of release to point of recovery in km/100. The following data are representative of one study using drift bottles to study ocean currents. Σx = 476, Σy = 87.1, Σx2 = 62,290, Σy2 = 2046.87, Σxy = 11121.3,and  r ≈ 0.94367. x days        72    76     32   91     205y km/100  14.7 19.6   5.3  11.7  35.8 a) Use a 1% level of significance to test the claim ? > 0.(Use 2 decimal places.)t =critical t= b) Find the predicted distance (km/100) when a drift bottle has been floating for 60 days. (Use 2 decimal places.)________ km/100 c) Find a 90% confidence interval for your prediction of part (d). (Use 1 decimal place.)lower limit = _____…

A chi square test tells us:  a. Whether two continuous variables are correlated with each other. b. Whether two discrete variables are independent of each other c. The amount of variation in one variable explained by the other d. Which categories of one variable are associated with which categories of the other variable

A researcher tested the relationship between gender and support for assisted suicide (“death with dignity”) and found a Cramer’s V of 0.45. This relationship is strong weak positive significant but not important

From the image:  One toad in the dataset had a body mass of 128g and a total tested mass of 163 mg. Compute the residual corresponding to this toad and write a sentence interpreting the residual.