wk 5 assignment
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University of Houston, Downtown *
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4317
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Industrial Engineering
Date
Dec 6, 2023
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docx
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3
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Wk. 5 Assignment
Wk. 5 Assignment
20.)
=CORREL(A2:A100, B2:B100)
21.)
=CORREL(B2:B100, C2:C100)
1.
Correlation between Age and Driving in Inclement Weather: The correlation coefficient between
age and driving in inclement weather is -0.21. This negative correlation indicates that as age
increases, the likelihood of driving in inclement weather decreases. However, the correlation is
weak, as the absolute value of the coefficient is less than 0.5.
2.
Statistical Significance: The correlation is not statistically significant at the 0.01 level. This means
that we cannot confidently claim that the observed correlation is not due to chance or random
variation in the data. Without statistical significance, we cannot establish a causal relationship
between age and driving in inclement weather.
3.
Probability of Driving in Inclement Weather for People Under 30: The probability that a randomly
selected person who drives in inclement weather is under 30 years old is 0.42. This probability is
determined by calculating the proportion of people under 30 who also drive in inclement
weather from the provided data.
4.
Probability of Driving in Inclement Weather for People Under 30: The probability that a randomly
selected person who is under 30 years old drives in inclement weather is 0.7. This probability is
calculated based on the proportion of people under 30 who drive in inclement weather from the
given dataset.
In conclusion, while there is a weak negative correlation between age and driving in inclement weather,
it is not statistically significant enough to establish a causal relationship. The probabilities indicate that
there is a relatively high likelihood of people under 30 years old driving in inclement weather, but
caution should be exercised in drawing strong conclusions without further analysis and evidence.
22.)
a.) Create a scatter plot:
b.) Calculate the slope:
=SLOPE(D2:D100, C2:C100).
c.) Calculate the intercept:
=INTERCEPT(D2:D100, C2:C100).
d.) Check linearity:
Visually inspect the scatter plot to see if the relationship appears linear.
e.) Test the slope:
You can perform a hypothesis test to check if the slope is significantly different from 0.
f.)
Comment on assumption violations:
Wk. 5 Assignment
Discuss potential violations of linear regression assumptions, such as linearity, independence of errors,
and homoscedasticity.
g.) Calculate the standard error of the estimate:
=STEYX(D2:D100, C2:C100).
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