Answer:
46.6 ft/sec (to the nearest tenth)
Step-by-step explanation:
There's a common identity useful here: 60 mph = 88 ft/sec.
Mult. 31.8 mph by the conversion factor (88 ft/sec) / (60 mph), obtaining:
46.64 ft/sec.
Answer:
Step-by-step explanation:
Subtract 5 from both sides.
Simplify.
Multiply both sides by 4.
Simplify.
Divide both sides by 3.
Simplify.
Answer: a. iv. The weight of a car can be used to explain about 79.6% of the variability in gas mileage using a linear relationship.
b. ii. There is a fairly strong, negative relationship between car weight and miles per gallon.
Step-by-step explanation:
- A coefficient of determination (denoted by R²) is a measure in a regression model that determines proportion of the variance in the dependent quantity that is predictable from the independent quantity.
- It is square of correlation coefficient (R).
Here, independent quantity = weight of a car
dependent quantity = miles per gallon (gas mileage)
The coefficient of determination (R²) was reported to be 79.6%.
That means, The weight of a car can be used to explain about 79.6% of the variability in gas mileage using a linear relationship.
- A correlation coefficient(R) tells about the strength and direction of relation .
- It lies between -1 and 1.
For the study, the correlation coefficient R is -0.8921.
There is a fairly strong, negative relationship between car weight and miles per gallon.
Answer:
Because it increases the risk of Type 1 error
Step-by-step explanation:
ANOVA is the analysis of the variance .
When comparing more than two treatment means we use ANOVA because a t test increases the risk of type 1 error .
For example if we wish to compare 4 population means there will be 4C2 = 6 separate pairs and to test the null hypothesis that all four population means are equal would require six two sample t test. Similarly to test 10 population mean would require 45 separate two sample t test.
This has two disadvantages .
First the procedure is too lengthy and tediuos.
Second the overall level of significance greatly increases as the number of t- tests increases.
The analysis of the variance compares two different estimates of variance using the F distributionto determine whether the population means are equal.
Probably the property of subtraction.
-TTL
