Question

             

The scatterplot below shows Olympic gold medal performances in the long jump from 1900 to 1988. The long jump is measured in meters. 9.0 8.8 8.6 8.4 E 8.2 8.0 7.8 -7,6 7.4 0 7.21o 7.0 0 20 40 60 8100 120 Year since 1900 Here is the equation of the least squares regression line Predicted long jump 7.24 + 0.014* year since 1900. What does the vertical-intercept (aka. y-intercept) tell us? 7.24 meters is the predicted value for the long jump in 1900 7.24 meters is the actual value for the long jump in 1900 7.24 is the minimum value for the long jump from 1900 to 1988 7.24 meters is the predicted increase in the winning long jump distance for each additional year after 1900. Wedding expenses and marriage length: With the headline, "Want a happy marriage? Have a big, cheap wedding" CNN reported on a study that examined the correlation between wedding expenses and the length of marriages. The news article states, "A new study found that couples who spend less orn their wedding tend to have longer-lasting marriages than those who splurge." http://www.cnn.com/2014/10/13/living/wedding-expenses-studyle What would be a reasonable explanation for the observed correlation? Having an inexpensive wedding helps young couples avoid financial burdens that may strain their marriage. Having an inexpensive wedding has no impact on the length of marriage because the cost of a wedding is a confounding variable that explains the correlation. Having an inexpensive wedding guarantees a couple will have a long-term marriage because the study shows a strong correlation between the two variables For a linear regression model, why would we want a small se? A small se means that the variation in the response variable is accounted for by the explanatory variable. A small se means that less of the variation in the response variable is explained by the explanatory variable A small se means the typical amount of prediction error for the model will be small. A small se means that the observed data values are close to the predicted data values on the regression line The scatterplot below shows Olympic gold medal performances in the long jump from 1900 to 1988. The long jump is measured in meters. 9.0 8.8 8.6 8.4 E 8.2 8.0 7.8 0 7.4 7.21 7.0 o 0 20 40 60 8100 120 Year since 1900 For this linear regression model, r 2-0.90. What does this mean? The maximum long jump was around 90 inches. ach year the winning long jump distance increased by 90%. 90% of the variation in long jump distances is explained by the year The data ends around 1990

Answer 1

Ans:

1)7.24 meters is the actual value for the long jump in 1900.

2)A small se mean that observed value are close to the predicted data values on the regression line.

3)second option is correct(cost of wedding is confounding variable)

4)

90% of the variation in long jump distances is explained by the year.