Statistics - Multiple Regression Projects

Are a person’s brain size and body size predictive of his/her intelligence? Data on the intelligence based

Are a person’s brain size and body size predictive of his/her intelligence? Data on the intelligence based on the performance IQ (PIQ) scores from the Wechsler Adult Intelligence Scale (revised), brain size based on the count from MRI scans (given as count/10,000), and body size measured by height in inches and

weight in pounds on 38 college students are shown below.

The data is presented below. Use SPSS to help you answer the following questions.

Note: You may copy and paste the data in SPSS but make sure the data is entered correctly. Data entry errors automatically reduce you one letter grade (i.e., 10 points).

Performance IQ	MRI	Height	Weight
124	81.69	64.5	118
150	103.84	73.3	143
128	96.54	68.8	172
134	95.15	65	147
110	92.88	69	146
131	99.13	64.5	138
98	85.43	66	175
84	90.49	66.3	134
147	95.55	68.8	172
124	83.39	64.5	118
128	107.95	70	151
124	92.41	69	155
147	85.65	70.5	155
90	87.89	66	146
96	86.54	68	135
120	85.22	68.5	127
102	94.51	73.5	178
84	80.8	66.3	136
74	93	74	148
86	88.91	70	180
84	90.59	76.5	186
134	79.06	62	122
128	95.5	68	132
102	83.18	63	114
131	93.55	72	171
84	79.86	68	140
110	106.25	77	187
72	79.35	63	106
124	86.67	66.5	159
132	85.78	62.5	127
137	94.96	67	191
110	99.7	75.5	192
86	88	69	181
81	83.43	66.5	143
128	94.81	66.5	153
124	94.94	70.5	144
94	89.4	64.5	139
89	93.59	75.5	179

Using SPSS, develop the estimated regression equation to predict intelligence (IQ) from MRI and weight.

For all of the variables of interest, report descriptive statistics (i.e., mean, median, standard deviation, and sample size). Based on this statistical information, describe the shape of your distributions (i.e., approximately normally distributed, negative, or positive skew).
Generate a correlation matrix including all variables of interest. Report and interpret the correlations within the context of this example.
Use SPSS to generate a regression line for the full model. Report this model. Interpret the appropriate effect size statistic, intercept and the partial regression statistics, if appropriate.
Using the statistics in SPSS, determine if the predictors are statistically significant for the full model. Report F, df, and p-value at .05. Make a decision, provide statistical evidence, and make a final conclusion within the context of this example.
Use SPSS to generate a regression line for each reduced model. Report each model. Interpret the appropriate effect size statistic, regression coefficients (i.e., slope, intercept), if appropriate.
Using the statistics in SPSS, determine if each predictor is statistically significant for each reduced model. Report t, df, and p-value at .05. Make a decision, provide statistical evidence, and make a final conclusion within the context of this example for each reduced model.

Compare the full model with a reduced model including only MRI. Report the t-statistic, df, and p-value. Make a decision, provide statistical evidence, and make a final conclusion. Based on your conclusion, which model is better?
Consider the full model with a reduced model including only MRI. Report this 95% confidence interval from SPSS. Interpret this confidence interval within the context of this example.
Compare the full model with a reduced model including only weight. Report the t-statistic, df, and p-value. Make a decision, provide statistical evidence, and make a final conclusion. Based on your conclusion, which model is better?
Consider the full model with a reduced model including only weight. Report this 95% confidence interval from SPSS. Interpret this confidence interval within the context of this example.
Is multicollinearity a problem? Provide statistical evidence.
Of the 3 models, what is your final decision about the ‘best’ model?

Price: $27.49

Solution: The downloadable solution consists of 14 pages, 1349 words and 17 charts.
Deliverable: Word Document