SPSS Analysis

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Psychological Testing (SPSS/Statistics Exercise)

Student Name:
Statistics Exercise
Complete the following exercises and submit for grading by the end of this week.  No statistical software is required; you should be able to execute all of the mathematical operations with a standard calculator.  Type your answers directly into this document and Save.
Suppose you have magically changed places with the professor teaching this course and that you have just administered an examination that consists of 100 multiple-choice items (where 1 point is awarded for each correct answer). The distribution of scores for the 25 students enrolled in your class could theoretically range from 0 (none correct) to 100 (all correct).   Below are your student’s scores.  You will use this raw data to complete all of the calculations in this assignment:

Student Score (number correct)
Judy 78
Joe 67
Lee-Wu 69
Miriam 63
Valerie 85
Diane 72
Henry 92
Esperanza 67
Paula 94
Urian 69
Leroy 61
Ronald 96
Vinnie 73
Bianca 79
Martha 62
Bill 61
Homer 44
Robert 66
Michael 87
Jorge 76
Mary 83
Mousey 42
Barbara 82
John 84
Donna 51

 
One task at hand is to communicate the test results to your class. You want to do that in a way that will help students understand how their performance on the test compared with the performances of other students. Probably, the first step is to arrange the data by converting it from a casual listing of raw scores into something that immediately provides a little more information.
Display (in descending order) the test scores and complete the table below. (6 points)

Scores from Your Test (X) Score f (frequency) f(X)
 

 
(2 points each):
 

  1. Identify the median of the frequency distribution. Median =

 

  1. Identify the mode in the frequency distribution. Mode(s) =

 

  1. What is the range of this frequency distribution? Range =

On average, how much does each score in this distribution vary from the mean score?
The steps for calculating the average deviation (AD) of a frequency distribution is as follows:

  1. Determine the deviation scores for each score in the frequency distribution (in other words, how much does each individual score vary from the mean score?).
  2. Find the sum of the deviation scores.
  • Divide the sum of the deviation scores by the total number of scores to obtain the average deviation.

Complete the table below (10 points).

Scores from Your Test(X) Score f(frequency) Absolute Valueof (X−x)
 

 

  1. The sum of the absolute value of deviation scores = (2 points)
  2. The total number of scores in the frequency distribution =
  3. Therefore, average deviation (AD) = (2 points)

What is the standard deviation of this distribution?
The standard deviation is equal to the square root of the average squared deviations about the mean. More succintly, it is equal to the square root of the variance. So one way to calculate the standard deviation of a frequency distribution is to calculate the variance. Complete the table below as the first step in calculating the variance:
(10 points)

X f X−x (X−x)2
 
 

 
(2 points each)

  1. The sum of the squared values of deviation scores =
  2. Variance = Sum of the squared values of deviation scores ÷ total number of scores
  3. Therefore, variance =
  4. Standard deviation = √Variance =

Think about how you will communicate this data to the class.
(2 points each)

  1. What type of frequency distribution would you use?
  2. Which type of graph would you use to represent the data?
  3. Which measure of central tendency would you use to represent the data?
  4. Which measure of variability would you use to represent the data?

It may be meaningful to your students to reference a normal curve when communicating the results.  This may be accomplished by calculating z scores and T scores.
z scores
The formula for calculating z scores is as follows:
In the equation, x is the mean of the frequency distribution and S is the standard deviation of the frequency distribution.  Complete the table by calculating the z score.
(25 points)
 

X f X−x z = (X−x) ÷ S
 

 
T scores = 10z + 50
Complete the table by calculating the T score.
(25 points)

Score f (frequency) T
 

 

Time Series: ARMA Models

Time Series: ARMA Models, Instructions; 
Check the attached paper and solve the five questions

Calculating sums of squares, variance, standard deviation, standard error and 95% confidence interval

Question 
The time taken for 21 heavy smokers to fall off a treadmill at the fastest setting is (18, 16, 18, 24, 23, 22, 22, 23, 26, 29, 32, 34, 34,
36, 36, 43, 42, 49, 46, 46, 57) minutes.
Calculate the:

  • sums of squares,
  • variance,
  • standard deviation,
  • standard error and
  • 95% confidence interval of these data.

SPSS/Statistics (Questions & Answers)

SPSS/Statistics (Questions & Answers),
Everything you need to know about:

  • Predictor variable
  • Probability distribution
  • Qualitative methods
  • Quantitative methods
  • Quartile
  • Randomization
  • Range
  • Ratio variable
  • Reliability
  • Repeated-measures design
    Second quartile
  • Skew
  • Systematic variation
  • Tertium quid
  • Test–retest reliability
  • Theory
  • Unsystematic variation
  • Upper quartile
  • Validity
  • Variables
  • Within-subject design
  • z-scores

SPSS/Statistics (Questions & Answers)

SPSS/Statistics (Questions & Answers)
Everything you need to know about: 

  • α-level
  • β-level
  • Central limit theorem
  • Confidence interval
  • Degrees of freedom
  • Deviance
  • Effect size
  • Fit
  • Linear model
  • Meta-analysis
  • One-tailed test
  • Population
  • Power
  • Sample
  • Sampling distribution
  • Sampling variation
  • Standard deviation
  • Standard error
  • Standard error of the mean (SE)
  • Sum of squared errors (SS)
  • Test statistic
  • Two-tailed test
  • Type I error
  • Type II error
  • Variance

SPSS/Statistics (Questions & Answers)

SPSS/Statistics (Questions & Answers)
Anything you need to know about:

  • Bootstrap
  • Hartley’s FMax
  • Homogeneity of variance
  • Independence
  • Kolmogorov–Smirnov test
  • Levene’s test
  • Noniles
  • Normally distributed data
  • Parametric test
  • Percentiles
  • P–P plot
  • Q–Q plot
  • Quantiles
  • Robust test
  • Shapiro–Wilk test
  • Transformation
  • Trimmed mean
  • Variance ratio

SPSS/Statistics (Questions & Answers)

SPSS/Statistics (Questions & Answers)
Anything you need to know about:

  • Bootstrap
  • Hartley’s FMax
  • Homogeneity of variance
  • Independence
  • Kolmogorov–Smirnov test
  • Levene’s test
  • Noniles
  • Normally distributed data
  • Parametric test
  • Percentiles
  • P–P plot
  • Q–Q plot
  • Quantiles
  • Robust test
  • Shapiro–Wilk test
  • Transformation
  • Trimmed mean
  • Variance ratio

Correlation Analysis SPSS/Statistics (Questions & Answers)

Correlation Analysis SPSS/Statistics (Questions & Answers),
Everything you need to know about:

  • Biserial correlation
  • Bivariate correlation
  • Coefficient of determination
  • Covariance
  • Cross-product deviations
  • Kendall’s tau
  • Partial correlation
  • Pearson correlation coefficient
  • Point–biserial correlation
  • Semi-partial correlation
  • Spearman’s correlation coefficient
  • Standardization

Regression Analysis SPSS/Statistics (Questions & Answers)

Regression Analysis SPSS/Statistics (Questions & Answers)
Everything you need to know about:

  • Adjusted predicted value
  • Adjusted R2
  • βi
  • Cook’s distance
  • Covariance ratio (CVR)
  • Cross-validation
  • Deleted residual
  • DFBeta
  • DFFit
  • Dummy variables
  • Durbin–Watson test
  • F-ratio
  • Generalization
  • Goodness of fit
  • Hat values
  • Heteroscedasticity
  • Hierarchical regression
  • Homoscedasticity
  • Independent errors
  • Leverage
  • Mahalanobis distances
  • Mean squares
  • Model sum of squares
  • Multicollinearity
  • Multiple R
  • Multiple regression
  • Outcome variable
  • Perfect collinearity
  • Predictor variable
  • Residual
  • Residual sum of squares
  • Shrinkage
  • Simple regression
  • Standardized DFBeta
  • Standardized DFFit
  • Standardized residuals
  • Stepwise regression
  • Studentized deleted residuals
  • Studentized residuals
  • Suppressor effects
  • t-statistic
  • Tolerance
  • Total sum of squares
  • Unstandardized residuals
  • Variance inflation factor (VIF)
  • Autocorrelation
  • bi