Anna Audler Stats 10: Section 3A UID: 304295761 TA: Luis Sosa Homework #3 4.2: A) Number of Square Feet because the points are less scattered in a linear direction B) Number of Square Feet because there is a stronger relationship between the total value of the homes and the number of square feet due to there being less vertical spread 4.4: There is no trend or relationship due to the data being very scattered. 4.8: The trend shows that people with a college degree tend to have a higher median family income. 4.11: Linear regression isn’t appropriate because the trend is curved 4.14: The scatterplot is curved, so it would not make sense to find correlation with the data. According to the data, the age with the highest fertility rate is about 28. 4.18: -0.903 = B 0.374 = A 0.777 = C 4.22: A) About 130-140 B) -4.494 + 14.66(10) = 142.106 4.32: A) The line for men, which means that men are predicted to weigh more than the women in the stats class B) The line for men, which means that as their age increases, their weight also increases at a faster rate than women 4.39: A) The graph shows that young and old drivers have more fatalities and the safest drivers are between the ages of 40 and 60 B) Not an appropriate linear regression since the trend is not linear 4.55: A) The salary is $2.099 thousand less for each year after the person was hired and $2.099 thousand more for each year before B) The intercept at $4,255,000 is the starting salary at year 0, which would be considered ridiculous.4.59: A) Positive Correlation B) For each dollar spent, the expenditure per pupil increases by 23 cents C) Represents the mean cost of education excluding the cost of paying the teachers, however this requires extrapolation which would make it invalid 4.61: A) Negative Correlation B) For each hour of work, the score went down by 0.48 points C) A student who didn’t do any work is expected to get on average an 87 4.64: A) Slope = 0.767; Intercept = 15.931; € ySon = 15.931+ 0.767xFather!Father=74in: Son = 15.931 + 0.767(74) = 72.689in predicted Father=65in: Son = 15.931 + 0.767(65) = 65.786in predicted B) Slope = 0.757; Intercept = 16.975; € yFather = 16.975 + 0.757 x Son!Son=74in: 16.975 + 0.757(74) = 72.993in predicted Son=65in: 16.975 + 0.757(65) = 66.18in predicted C) Regression towards the mean: Predicted values that are far from the mean lead to values that are closer to the
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