At least one of your variables must be quantitative. Save all of your files into this directory so that they can be accessed from different machines. Once you have located the information that you wish to use and saved it to the file server, the instructor will help you get the information into SPSS.
Produce the following output with SPSS. Annotate the output to include the bibliographic citation and any interesting information you found. Sample output is available.
Collect data on the price to get from one city to another. In each case, select the cheapest fare that meets the given conditions. You may depart at anytime on the indicated dates. After you have entered your information into the template, save it under the name "travel " where is the number of your internet site.
The instructor will go through and compile all the data into one master file called "travel" for you to use. Then find the mean fare of all flights. Compare the mean to the overall value using a one-sample t test. Be sure to select all cases when done. Then compare the fares using the independent samples t-test with sat as the grouping variable.
Rerunning our minimal regression analysis from A nalyze R egression L inear gives us much more detailed output. The screenshots below show how we'll proceed. Unfortunately, SPSS gives us much more regression output than we need. We can safely ignore most of it. However, a table of major importance is the coefficients table shown below.
This table shows the B-coefficients we already saw in our scatterplot. As indicated, these imply the linear regression equation that best estimates job performance from IQ in our sample. It's statistically significantly different from zero. So B is probably not zero but it may well be very close to zero. The confidence interval is huge -our estimate for B is not precise at all- and this is due to the minimal sample size on which the analysis is based.
Apart from the coefficients table, we also need the Model Summary table for reporting our results. R is the correlation between the regression predicted values and the actual values. For simple regression, R is equal to the correlation between the predictor and dependent variable. R Square -the squared correlation- indicates the proportion of variance in the dependent variable that's accounted for by the predictor s in our sample data.
Adjusted R-square estimates R-square when applying our sample based regression equation to the entire population. Adjusted r-square gives a more realistic estimate of predictive accuracy than simply r-square. In any case, this is bad news for Company X: IQ doesn't really predict job performance so nicely after all. Next, assumptions are best evaluated by inspecting the regression plots in our output. If normality holds, then our regression residuals should be roughly normally distributed. The histogram below doesn't show a clear departure from normality.
The regression procedure can add these residuals as a new variable to your data. By doing so, you could run a Kolmogorov-Smirnov test for normality on them. For the tiny sample at hand, however, this test will hardly have any statistical power. So let's skip it. The 3. This is a scatterplot with predicted values in the x-axis and residuals on the y-axis as shown below.
Both variables have been standardized but this doesn't affect the shape of the pattern of dots. Honestly, the residual plot shows strong curvilinearity. I manually drew the curve that I think fits best the overall pattern. Assuming a curvilinear relation probably resolves the heteroscedasticity too but things are getting way too technical now. The basic point is simply that some assumptions don't hold. The most common solutions for these problems -from worst to best- are.
The figure below is -quite literally- a textbook illustration for reporting regression in APA format. Creating this exact table from the SPSS output is a real pain in the ass.
0コメント