Run a linear regression of time spent studying (Study; independent) predicting time spent watching TV (TV; dependent). Copy and paste your output and answer the following questions.
Regression
Descriptive Statistics
Mean
Deviation
How much time do you spend watching TV per week (in hours)?
How much time do you spend studying per week (in hours)?
Correlations
How much time do you spend watching TV per week (in hours)?
How much time do you spend studying per week (in hours)?
Pearson Correlation
How much time do you spend watching TV per week (in hours)?
How much time do you spend studying per week (in hours)?
Sig. (1-tailed)
How much time do you spend watching TV per week (in hours)?
.002
How much time do you spend studying per week (in hours)?
.002
N
How much time do you spend watching TV per week (in hours)?
50
50
How much time do you spend studying per week (in hours)?
50
50
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
How much time do you spend studying per week (in hours)?b
Enter
a. Dependent Variable: How much time do you spend watching TV per week (in hours)?
b. All requested variables entered.
Model Summaryb
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.397a
.158
.140
11.9242
a. Predictors: (Constant), How much time do you spend studying per week (in hours)?
b. Dependent Variable: How much time do you spend watching TV per week (in hours)?
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1
8.983
.004b
Residual
48
Total
49
a. Dependent Variable: How much time do you spend watching TV per week (in hours)?
b. Predictors: (Constant), How much time do you spend studying per week (in hours)?
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95.0% Confidence Interval for B
B
Std. Error
Beta
Lower Bound
Upper Bound
1
(Constant)
2.386
2.756
.866
.391
-3.156
7.927
How much time do you spend studying per week (in hours)?
.878
.293
.397
2.997
.004
.289
1.467
a. Dependent Variable: How much time do you spend watching TV per week (in hours)?
Residuals Statisticsa
Minimum
Maximum
Mean
Std. Deviation
N
Predicted Value
2.386
28.734
8.920
5.1055
50
Residual
-19.9511
56.2662
.0000
11.8019
50
Std. Predicted Value
-1.280
3.881
.000
1.000
50
Std. Residual
-1.673
4.719
.000
.990
50
a. Dependent Variable: How much time do you spend watching TV per week (in hours)?
1. Is the model good (statistically significant)? Write a sentence, using the proper format, describing the results of the ANOVA test.
The model is statistically significant as there is moderate correlation between how much time a student studies and how much time a student spends watching TV, as R = .397, p < 0.05.
2. Would it be appropriate to report the value of R-squared in the Model Summary? Why or why not? If yes, report and interpret. If no, write N/A.
Yes, it is meaningful at slightly less than 16% but it is not a great predictor. R-squared = .158.
3. Is time spent studying a statistically significant predictor of time spent watching TV according to the coefficients box? Write a sentence, using the proper format, describing the results.
Yes, the standardized beta is .397, p < 0.05 and is statistically significant.
4. Look at the b coefficient. By how much would we predict time spent watching TV to change for every 1 hour increase in time spent studying? Will it increase or decrease by this amount?
.878 hours increase
Task 2. Run a linear regression of time spent studying and time spent working (Study and Work; independents) predicting time spent watching TV (TV; dependent). Copy and paste...
Carroll, J.S., Williams, M. & Gallivan, T.M. (2012). The ins and outs of change of shift handoffs between nurses: A communication challenge. BMJ Qual Saf (2012). doi:10.1136/bmjqs-2011-000614 Shift handoffs can be used as benchmarks to assess quality of communications among healthcare staff. Because communication overall is critical to quality of care, it is important to understand the factors that can improve shift handoff efficiency. Shift handoffs involve both technical communication and
Ameen Masoudi Combination Therapeutic Exercises with Manipulation for Reliving Pain and Increasing Range of Motion for Non-Specific Chronic Low Back Pain The lumbar spine is composed of the vertebrae as well as the ligaments beside the discs, nerves and muscles. This area is a common source of pain. This is one of reasons why people visit their physicians. Around seventy percent of people who live in advanced countries will have lower back pain
Memory Recall Author(s) First, Middle Initial (if applicable) and Last Name(s) in Starting with the Individual who Made the Biggest Contribution (not alphabetical) This study examines the difference that categorization makes in memory recall exercises. It uses students from Queens College in an experiment in which categorized words are read aloud to one group and random words read aloud to a second group. The groups are then scored according to how many
Nonparametric Analysis of Data Set The datasets consist of the following variables: • Sub_num • Gender • Major • Coffee, and • Num_cups. The datasets are used for the nonparametric analysis to investigate whether the choices of college majors are different between males and female's individuals. The analysis is carried out with the chi-square test and the document presents the output as follows: Gender * Major Cross-tabulation Count Major Total Gender Total Chi-Square Tests Value Df Asymp. Sig. (2-sided) Pearson Chi-Square Likelihood Ratio Linear-by-Linear Association N of Valid Cases The minimum
The data collected will be ordinal, largely strongly agree, agree, disagree, strongly disagree type of answers. This is practical because we are trying to gauge not only whether the sentiment exists, but the intensity of the sentiment on the part of the consumer. The decision rule that will confirm or deny the hypothesis is the correlation of responses indicating fear of West Nile and responses indicating that this drives a
If the numbers or the data will not be constant, the results will not be paralleled and will surely have discrepancy on its accuracy. Meanwhile, experimenter bias is a big threat to the accuracy and/or precision of the report's result. An example of the threat is not being able to identify all the factors that might affect the result. An experiment can be said biased if he/she has already an
Our semester plans gives you unlimited, unrestricted access to our entire library of resources —writing tools, guides, example essays, tutorials, class notes, and more.
Get Started Now