Computer Assignment 2: Statistics Analysis and T-tests - SPSS

This assignment solution analyzes two statistical scenarios using t-tests within the context of basic business statistics. Part I examines object recognition differences between right and left-handed individuals using an independent samples t-test, concluding no significant difference based on a p-value greater than alpha. Part II investigates the impact of different motivation types on task performance, employing a t-test and regression analysis. The analysis reveals a significant difference in performance based on motivation type, with a positive but weak relationship between tasks and motivation. The solution includes SPSS outputs, hypothesis statements, statistical test results, and conclusions, along with a bibliography of relevant sources.

Running head: BASIC BUSINESS STATISTICS
Basic Business Statistics
Name of the student:
Name of the University:
Author note:

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BASIC BUSINESS STATISTICS
Table of Contents
Part I. Handedness and Recognition..........................................................................................2
Part II. Types of Motivation and Performance...........................................................................3
Bibliography...............................................................................................................................7

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Part I. Handedness and Recognition
Null hypothesis (H0): There is no differences between the right hand and left hand participant
on object recognition.
Alternative hypothesis (Ha): There is a differences between the right and left hand participant
on object recognition.
Table t-test output
It has been seen that the mean of the right hand people work recognition is 9.95. The
standard deviation and standard error of this variable is 2.605 and .583.
Similarly the mean of the left hand people work recognition is 8.65. The
standard deviation and standard error of this variable is 2.159 and .483.
It is a two tailed test.
Result:
Test statistic (t) = 1.589
Alpha = 0.05

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P-value = 0.128
Conclusion:
It has been seen that the P-value > alpha. Hence the null hypothesis is not significant.
Thus it may be summarized that there is no differences between the right hand and left hand
participant on object recognition.
Part II. Types of Motivation and Performance
Null hypothesis (H0): There is no differences on the effects of different types of motivation
among the performance of tasks.
Alternative hypothesis: There is a differences on the effects of different types of motivation
among the performance of tasks.

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Table 2 Descriptive Statistics

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Table 3 Levene test
In group the mean of motivation is 12.60. Similarly the standard deviation and
standard error is 2.85 and 0.520.
In individual the mean of motivation is 10.40. Similarly the standard deviation and
standard error is 3.21 and 0.586.
From this test it has been seen that the variances for both groups are not equal. The
individual has a higher variability as compared to group.
Table 4 t-test output
Test statistic = 2.807
Alpha = 0.05
P-value= 0.007
It is clear that P-value < alpha. Hence the null hypothesis is significant. Thus it may
be summarized that there is a differences on the effects of different types of motivation
among the performance of tasks.

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Table 5 Regression output
The regression model is
Tasks= 14.80+ (-2.20) * Motivation
The value of the r-square= 0.120
Hence the relationship between tasks and motivation is positive but not strong.
Conclusion:
 There is a differences on the effects of different types of motivation among the
performance of tasks.
 The relationship between tasks and motivation is positive but not strong.

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Bibliography
De Winter, J. C. (2013). Using the Student's t-test with extremely small sample sizes.
Practical Assessment, Research, and Evaluation, 18(1), 10.
Fox, J. (2019). Regression diagnostics: An introduction (Vol. 79). SAGE Publications,
Incorporated.
Pallmann, P., Hothorn, L. A., & Djira, G. D. (2014). A Levene-type test of homogeneity of
variances against ordered alternatives. Computational Statistics, 29(6), 1593-1608.
Vogt, A., & Barta, J. (2013). The making of tests for index numbers: Mathematical methods
of descriptive statistics. Springer Science & Business Media.
Zholud, D. (2014). Tail approximations for the Student $ t $-, $ F $-, and Welch statistics for
non-normal and not necessarily iid random variables. Bernoulli, 20(4), 2102-2130.

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