Psychology Week four assignment

Week 4 Assignment

For this assignment you will be using the Regression file. You will use this file again for next week's assignment. The file contains several variables:

· Subjno = Subject number

· Timedrs = Number of visits to health professionals (DV)

· Phyheal = Number of physical health symptoms (IV)

· Menheal = Number of mental health symptoms (IV)

· Stress = Stressful life events score (IV)

For this week's assignment, we are only focusing on two variables (timedrs and stress)

1. Outliers

As with all data, it is important to examine potential univariate and bivariate outliers. Use procedures described in earlier assignments to examine outliers and to get to know your data. Be sure to include:

· Z-score analysis

· Box plot visualization

· Tests of normality

If you see potential outliers, describe them and how you would handle them (i.e., keep, delete or transform) but do NOT do this as I would like everyone to work with the same data set.

Before conducting any analyses, examine the data for potential outliers and ensure it meets key assumptions for regression. Follow these steps:

Univariate Outliers: Use z-scores and boxplots to identify any outliers in timedrs and stress.

· Identify values with z-scores > ±3.3

· Are the distributions approximately normal?

· Discuss whether these values appear to be true outliers and how you would handle them (e.g., retain, transform, or exclude) but do not remove any outliers for this assignment

2. Scatterplot Creation

Create a scatterplot with Stress (IV) on x-axis and timedrs (DV) on y-axis.

[Insert scatterplot here]

Label scatterplot: Figure X Scatterplot of {IV} and {DV}

Interpretation: Based on visual inspection of the scatterplot:

· Describe the relationship between [Independent Variable] and [Dependent Variable] appears to be [positive/negative/no clear relationship]

· Comment on the strength of the relationship (e.g., weak/moderate/strong)

· Note any potential outliers or unusual patterns in the data (e.g., clusters, gaps)

· Do the data points follow a linear pattern?

3. Correlation Analysis

Conduct a Pearson correlation analysis between stress and timedrs.

Report results:

A Pearson correlation analysis was conducted to examine the relationship between [Independent Variable] and [Dependent Variable].

Results: r(df) = [correlation coefficient], p = [p-value]

Interpretation:

· Sign: Is the correlation positive or negative?

· Strength: Use standard benchmarks to describe the strength:

· Weak: 0.1≤r<0.30

· Moderate: 0.3≤r<0.50

· Strong: r≥0.5

· Direction: Explain what the relationship means in context

The correlation is [positive/negative], indicating that as [Independent Variable] [increases/decreases], [Dependent Variable] tends to [increase/decrease] as well.

4. Regression Analysis

Conduct a simple linear regression with stress as the independent variable (IV) and timedrs as the dependent variable (DV).

You are trying to predict the number of visits a person takes to the doctor with how much stress they have in their life.

Results Reporting:

1. Significance of Regression:

A simple linear regression was conducted with [Independent Variable] as the independent variable and [Dependent Variable] as the dependent variable.

Report the overall model significance using the F-test results.

2. Model Fit:

Report R² and interpret the percentage of variance explained:

· Model fit: R² = [value], indicating that [percentage]% of the variance in [Dependent Variable] is explained by [Independent Variable].

3. Regression Equation:

Report the regression equation using the intercept and slope coefficients:

[Dependent Variable] = [intercept] + [slope] * [Independent Variable]

Interpret each term:

· The intercept (value) represents the predicted [dependent variable] when [independent variable] is zero

· The slope (value) indicates how much each one-unit increase in [independent variable], the dependent variable is predicted to [increase/decrease] by [value] units

4. Assumption Checks

· Linearity: Confirm linearity was assessed earlier with the scatterplot. Summarize findings here.

· Normality of residuals:

· Create a histogram or Q-Q plot of residuals

· Discuss whether residuals are approximately normally distributed

5. Comparison of correlation and regression

· Correlation coefficient (r = [value]):

· Measures the strength and direction of the relationship between variables, independent of units

· Standardized measure ranging from -1 to +1

· Dimensionless and independent of the scale of the variables

· Regression slope (b = [value]):

· Represents the rate of change in [Dependent Variable] for each one-unit increase in [Independent Variable]

· Maintains the units of the original variables

· Depends on the scale of the variables

[Explain the difference between correlation coefficient and regression slope in context of the study]

Limitations

Discuss any limitations of the study (e.g., small sample size, presence of outliers, assumptions not fully met).

Conclusion

Write a paragraph bringing together the findings and writing up the results in APA format. Include:

· The regression equation

· The significance of the model and its R² value

· Key results from the correlation and regression analyses

· Implications of the relationship between stress and doctor visits