Evaluate log4 0.25 ?

You and a group of friends are going to a five-day outdoor music festival during spring break. You hope it does not rain during

KIM [24]

Answer:

The probability of not rain during the entire festival is 0.19

Step-by-step explanation:

The Complement Rule states that the sum of the probabilities of an event and its complement must equal 1.

In this case we have the probability of raining of each day, we need the probability of NOT raining.

First day= 45% chance of rain, the complement is 55%

Second day 55% chance of rain, the complement is 45%

Third day a 10% chance of rain, the complement is 90%

Fourth day a 10% chance of rain, the complement is 90%

Fifth day 5% chance of rain, the complement is 95%

To get the probability of NOT raining in the entire festival is the multiplication of all the complements.

P(not raining) = 0.55 x 0.45 x 0.90 x 0.90 x 0.95= 0.19

4 0

2 years ago

A member of a student team playing an interactive marketing game received the fol- lowing computer output when studying the rela

nirvana33 [79]

Answer:

$p_v = 2*P(t_{n-2} > |t_{calc}|)= 0.91$

So on this case for the significance level assumed $\alpha=0.05$ we see that $p_v >\alpha$ so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.

So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.

Step-by-step explanation:

Let's suppose that we have the following linear model:

$y= \beta_o +\beta_1 X$

Where Y is the dependent variable and X the independent variable. $\beta_0$ represent the intercept and $\beta_1$ the slope.

In order to estimate the coefficients $\beta_0 ,\beta_1$ we can use least squares procedure.

If we are interested in analyze if we have a significant relationship between the dependent and the independent variable we can use the following system of hypothesis:

Null Hypothesis: $\beta_1 = 0$

Alternative hypothesis: $\beta_1 \neq 0$

Or in other words we want to check is our slope is significant (X have an effect in the Y variable )

In order to conduct this test we are assuming the following conditions:

a) We have linear relationship between Y and X

b) We have the same probability distribution for the variable Y with the same deviation for each value of the independent variable

c) We assume that the Y values are independent and the distribution of Y is normal

The significance level assumed on this case is $\alpha=0.05$

The standard error for the slope is given by this formula:

$SE_{\beta_1}=\frac{\sqrt{\frac{\sum (y_i -\hat y_i)^2}{n-2}}}{\sqrt{\sum (X_i -\bar X)^2}}$

Th degrees of freedom for a linear regression is given by $df=n-2$ since we need to estimate the value for the slope and the intercept.

In order to test the hypothesis the statistic is given by:

$t=\frac{\hat \beta_1}{SE_{\beta_1}}$

The p value on this case would be given by:

$p_v = 2*P(t_{n-2} > |t_{calc}|)= 0.91$

So on this case for the significance level assumed $\alpha=0.05$ we see that $p_v >\alpha$ so then we can conclude that the result is NOT significant. And we don't have enough evidence to reject the null hypothesis.

So on this case is not appropiate say that :"the more we spend on advertising this product, the fewer units we sell" since the slope for this case is not significant.

3 0

2 years ago

Megan consumed 3.5 gallons of water in one day. How many milliliters are equal to 3.5 gallons, if 1 liter = 1,000 milliliters an

baherus [9]

1 gallon = 3.785 liters

3.5 * 3.785 = 13.2475 total liters

1 liter = 1000 milliliters

13.2475 * 1000 = 13,247.5 milliliters

Answer is B

3 0

2 years ago

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Determine the values of c so that the following functions represent joint probability distributions of the random variables X an

Anna11 [10]

Answer:

The joint probility density will be c = 1/36

The joint probability distribution will be c = 1/15

Step-by-step explanation:

For step by step explanation see the pucture attached

7 0

2 years ago

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Jace gathered the data in the table. He found the approximate line of best fit to be y = –0.7x + 2.36. What is the residual valu

sammy [17]

The answer
the table is

x 0 1 4 5 7
y 3 1 0 -2 -2

<span>the approximate line of best fit to be y = –0.7x + 2.36
so when the value of x=5, the residual value is

</span><span>y = –0.7(5) + 2.36 = -1.14 this is the actual value
the predicted value is -2 (for x=5)

and residual value formula is RS = y actual value - y predicted value

so RS = -1.14 - (-2) = 0.86
</span>

3 0

2 years ago

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