now, we know 7 of those angles are already 835°, so the last one must be 1080 - 835 = 245°.
if you flip a coin and roll a 6 sided die, what is the probability that you will flip a tails and roll 2
2 answers:
Answer:
Step-by-step explanation:
The combinations:
heads and 1, heads and 2, heads and 3, heads and 4, heads and 5, heads and 6, tails and 1, tails and 2, tails and 3, tails and 4, tails and 5, tails and 6
- There are 12 combinations (count if you don't believe me). An easy way to find this is by multiplying the number of things you can get from the coin (2)and the number of things you can get from the die (6).
- There is only one combination where you can get tails and roll 2, so the probability will be
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Answer:
Since the p value is higher than the significance level assumed 0.05 we FAIL to reject the null hypothesis at 5% of significance, and we can conclude that we can assume that we have equally like results.
Step-by-step explanation:
A chi-square goodness of fit test "determines if a sample data matches a population".
A chi-square test for independence "compares two variables in a contingency table to see if they are related. In a more general sense, it tests to see whether distributions of categorical variables differ from each another".
Assume the following dataset:
Number: 1, 2 , 3 , 4 , 5 ,6
Frequency: 27, 31, 42, 40, 28, 32
We need to conduct a chi square test in order to check the following hypothesis:
H0: The outcomes are equally likely.
H1: The outcomes are not equally likely.
The level of significance assumed for this case is
The statistic to check the hypothesis is given by:
The observed values are given:
The expected values are given by:
And now we can calculate the statistic:
Now we can calculate the degrees of freedom for the statistic given by:
And we can calculate the p value given by:
And we can find the p value using the following excel code:
"=1-CHISQ.DIST(5.860,5,TRUE)"
Since the p value is higher than the significance level assumed 0.05 we FAIL to reject the null hypothesis at 5% of significance, and we can conclude that we can assume that we have equally like results.