Statistics Statistics





  1. The dependent variable is the player’s score.

  2. The hypothesis being tested is The score of a golf player is affected by the quality of play of the playing partners

  3. The variable that captures the hypothesis is the Score of the playing partners

  4. The coefficient is 0.035

  5. The meaning of this coefficient is that a player’s score will be affected by a margin of 0.035 of the scores of the playing partner. It shows the extent of change of an individual caused by the score of the playing partners.

  6. It is not at the significant 5% Instead, it is at 0.5%

  7. This means that there is a 0.5% chance of having the wrong value of the coefficient compared to the one given, 0.035.

  8. Would you say that there is good evidence for peer effects? Explain.

Thereis no good evidence for peer effects on the playing partner’s scoreto that of an individual player. This is because the coefficient ofthe equation is low that the change of the score of an individual isinsignificant to the change in the score of the playing partners.

  1. The coefficient on own ability is significant at the 5% level

  2. The type of variable is a constant variable

  3. This estimate means that the score of an individual will be a constant from a person’s ability. The rest of the value for full score will change depending on other factors, such as the










  1. If he wrestles better on Tuesday the best statistical test to use is the Null Hypothesis test

  2. The hypothesis to be tested Kobo performs better on Tuesdays than on Thursday

  3. The null hypothesis Kobo does not perform better on Tuesdays than on Thursday

  4. The p-value, 0.167 obtained from the test shows that Kobo should accept the hypothesis he was testing. It means that he should accept that he performs better on Tuesdays than on Thursdays. This is because the p-value is higher than the 0.05

  5. What is learned from this test is that the hypothesis is a true hypothesis. The hypothesis of the results provides proves that Kobo is lucky to win more on Tuesdays, just like his name suggests. This is because the p-value is greater than 0.05, thereby indicating that he should reject the null hypothesis.


Fromthe bowling exercises of Fred, the following are his dataxxx/xxx/////xxxx////x

  1. The conditional probability of a strike in the frame following a strike is 3/21. In terms of percentage, the probability is 3/21 (100) = 14.29%

  2. The number of runs for Fred are21, with 11 strikes and 10 spares

  3. After the runs test for this data relating to Fred, the Expected Number of Runs: 11.5 with a standard deviation of 2.2279 and a z-value of -2.0091

Theexpected number of runs shows the total number of outcomes thatexpected results to appear on the same consecutive flips. Thestandard deviation shows the level of deviation from the mean of allthe statistics or outcomes of the bowls. The resulting data does notshow any evidence of the hot hand phenomenon. This is because theprobability of strikes seems to be decreasing as the amount of triesincrease.

  1. The picture of the distribution of runs.