Answer:
let monday be a = 800 (nearest 100)
let tuesday be b = 700 (nearest 100)
a+b = 800 + 700 = 1500
a+b = 1500
If she picks all 3 red markers, she will win a total of $500. If the first marker she picks is red but not all 3 markers
are red, she will win a total of $100. Under any other outcome, she will win $0.
What is the expected value of Jessica's winnings?
Round your answer to the nearest cent.
Answer:
The probability of Jessica picking 3 consecutive red markers is: (1/6)
The probability of Jessica's first marker being red, but not picking 3 consecutive red markers is:
(3/5)−(1/6)=(13/30)
So i am bit stuck here
what i think is it shouldn't be that complex it should be as simple as chance of Jessica's first marker being red=chance of getting red 1 time i.e P(First marker being red)=(6/10) can any explain me the probability of Jessica's first marker being red=(13/30)?
Step-by-step explanation:
Answer:
$126.67
Step-by-step explanation:
Answer:
Step-by-step explanation:
Solution:-
State the hypotheses. The first step is to state the null hypothesis and an alternative hypothesis.
Null hypothesis: The distribution of severity of psoriasis cases at the end and prior are same.
Alternative hypothesis: The distribution of severity of psoriasis cases at the end and prior are different.
Formulate an analysis plan. For this analysis, the significance level is 0.05. Using sample data, we will conduct a chi-square goodness of fit test of the null hypothesis.
Analyze sample data. Applying the chi-square goodness of fit test to sample data, we compute the degrees of freedom, the expected frequency counts, and the chi-square test statistic. Based on the chi-square statistic and the degrees of freedom, we determine the P-value.
DF = k - 1 = 4 - 1
D.F = 3
(Ei) = n * pi
Category observed Num expected num [(Or,c -Er,c)²/Er,c]
Remission 380 20 6480
Mild
symptoms 520 770 81.16883117
Moderate
symptoms 95 160 24.40625
Severe
symptom 5 50 40.5
Sum 1000 1000 6628.075081
Χ2 = Σ [ (Oi - Ei)2 / Ei ]
Χ2 = 6628.08
Χ2Critical = 7.81
where DF is the degrees of freedom, k is the number of levels of the categorical variable, n is the number of observations in the sample, Ei is the expected frequency count for level i, Oi is the observed frequency count for level i, and Χ2 is the chi-square test statistic.
The P-value is the probability that a chi-square statistic having 3 degrees of freedom is more extreme than 6628.08.
We use the Chi-Square Distribution Calculator to find P(Χ2 > 19.58) =less than 0.000001
Interpret results. Since the P-value (almost 0) is less than the significance level (0.05), we cannot accept the null hypothesis.
We reject H0, because 6628.08 is greater than 7.81. We have statistically significant evidence at alpha equals to 0.05 level to show that distribution of severity of psoriasis cases at the end of the clinical trial for the sample is different from the distribution of the severity of psoriasis cases prior to the administration of the drug suggesting the drug is effective.
The chi-square test is a statistical method that determines if there's a significant difference between observed and expected frequencies in different categories, such as symptom status in this clinical trial. Without post-treatment numbers, we can't run the exact test. However, if the test statistic exceeded the critical value, we could conclude that the drug significantly affected symptom statuses.
This question pertains to the use of a chi-squared test, which is a statistical method used to determine if there's a significant difference between observed frequencies and expected frequencies in one or more categories. For this case, the categories are the symptom statuses (remission, mild, moderate, and severe).
To conduct a chi-square test, you first need to know the observed frequencies (the initial percentages given in the question) and the expected frequencies (the percentages after treatment). As the question doesn't provide the numbers after treatment, I can't perform the exact chi-square test.
If the post-treatment numbers were provided, you would compare them to the pre-treatment numbers using the chi-squared formula, which involves summing the squared difference between observed and expected frequencies, divided by expected frequency, for all categories. The result is a chi-square test statistic, which you would then compare to a critical value associated with a chosen significance level (commonly 0.05) to determine if the treatment has a statistically significant effect.
To interpret a chi-square test statistic, if the calculated test statistic is larger than the critical value, it suggests that the drug made a significant difference in the distribution of symptom statuses. If not, we can't conclude the drug was effective.
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Given: An urn contains 11 balls, 3 white, 3 red, and 5 blue balls.
You win $1 for each red ball you select and lose a $1 for each white ball you select.
Let X be the number of times you win.
The total number of ways to select 2 balls (order does not matter) =
The number of ways so that two balls are white (and
The number of ways so that two balls are red (and
The number of ways so that one ball is red, one is white (and
The number of ways so that two balls are blue (and ):
i.e.
The number of ways so that one ball is blue, one is white (and ):
The number of ways so that one ball is blue, one is red (and ):
Thus, the probability mass function (p.m.f.) of X would be ( in attachment) :
y=40- 3x-3
si 8888888888888888888888888888
The daily average dollar amount of transactions where there are 2.5 trillion in credit card transactions annually is 6.8 billion.
Given data:
To calculate the daily average dollar amount of transactions, we'll divide the annual total by the number of days in a year.
Annual credit card transactions: $2.5 trillion
Number of days in a year: 365
Daily average dollar amount of transactions = Annual transactions / Number of days
= $2.5 trillion / 365
Now let's perform the calculation:
Daily average dollar amount = $2.5 trillion / 365
On simplifying the equation:
Daily average dollar amount ≈ $6.849 billion
Rounded to the nearest hundred million dollars, the daily average dollar amount of credit card transactions is approximately $6.8 billion.
Hence, the daily average dollar amount of credit card transactions is approximately $6.8 billion.
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Answer:
3 trillion
Step-by-step explanation:
because just do the math bu bl I really dk