Questionl Researcher investigated a relationship between length (X) and weight (

Question

Questionl Researcher investigated a relationship between length (X) and weight () of snakes. H observed a linear trend and obtained following least squares regression line Longest ys 6.89 x-303.07 ·Length was measured in snake in centimeters, weight in grams. m the sample was 1.8 meters (180 em) long SST- 9811 and SSE-1194 vep poith Imterpret the slope of his regrssion line in the contet of the problem, be and give percentage of total variability among the weight of the snakes (Y) that is explained by the regression line. Round answers to 4 decimal places. b) (10 points) Compute coefficient of determination and linear correlation coefficient Linear correlation coefficient Percentage explained c) (5 points)Use the given equation to predict the weight of a snake that is 1.5 meters (150 cm) long. d) (5 points)Explain briefly why predicting a weight of a snake that is 2.2 meters (220cm) long may give unreasonable results

Explanation / Answer

a) regression line is y= 6.89x - 303.07

comparing this with standard line equation y= mx + c where m is slope and c is intercept

we get slope of regression = 6.89

b) SST = 9811

SSE = 1194

coefficient of determination = 1 -SSE/SST = 0.8783

Linear correlation coeffiicient= square root of coefficient of determination = 0.9372

percentage of variation = (1-coefficient of determination) *100 = 12.17

c) y = 6.89x - 303.07

given length ,x = 150 cm

weight, y= 6.89*150 -303.07 = 730.43 grams

d) Predicting a weight of 2.2m long snake will give unreasonable results because in our sample longest length of snake was 1.8m. So our regression model is not suitable for length 2.2m and it may give large errors.

Related Questions

Navigate

• Browse (All)
• Browse Q
• Subjects

---

---

Integrity-first tutoring: explanations and feedback only — we do not complete graded work. Learn more.