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Examtopics C2090-011: IBM SPSS Statistics Level 1 v2 Prepaway Real Exam Questions and dumps free download
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1476-C2090-011: IBM SPSS Statistics Level 1 v2
#Question
When setting up your variables in SPSS, which view should you use to define variable attributes?
A.
Output view
B.
Syntax view
C.
Variable view
D.
Data view
Correct Answer:
C
The Variable view allows you to define attributes such as name, type, width, and measurement scale for each variable.
#Question
In a study, you find a residual plot that shows a curve rather than a random scatter. What does this suggest?
A.
Homoscedasticity
B.
Strong correlation
C.
No problem with regression
D.
Non-linearity in data
Correct Answer:
D
A curved pattern in residuals signifies that the relationship may not be adequately captured by a linear model.
#Question
Which SPSS output would help identify outliers within a dataset?
A.
Descriptive statistics
B.
Boxplot
C.
Frequency table
D.
Correlation matrix
Correct Answer:
B
Boxplots visually indicate outliers in a dataset, showing values that lie far from the range of typical values.
#Question
A researcher wants to know if age influences job satisfaction in a dataset. Which analysis would be appropriate?
A.
Logistic regression
B.
Chi-square
C.
ANOVA
D.
Multiple regression
Correct Answer:
D
Multiple regression allows the assessment of the impact of age (continuous predictor) on job satisfaction (continuous outcome).
#Question
If conducting a regression analysis in SPSS and noticing non-significant predictors, what should you consider?
A.
Remove all predictors
B.
Check for multicollinearity
C.
Report results without changes
D.
Increase sample size
Correct Answer:
B
Checking for multicollinearity helps determine if predictor variables are redundant.
#Question
When creating a histogram in SPSS, which parameter is crucial for interpreting the distribution?
A.
Color of bars
B.
Axis labels
C.
Title of the graph
D.
Number of bins
Correct Answer:
D
The number of bins affects how well the histogram represents the underlying data distribution.
#Question
What is the purpose of using bootstrapping in statistical analysis?
A.
To reduce data variability
B.
To improve sample size
C.
To test regression models
D.
To create confidence intervals
Correct Answer:
D
Bootstrapping is a resampling technique used to create confidence intervals without assuming a normal distribution.
1476-C2090-011: IBM SPSS Statistics Level 1 v2
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