Question 1164885
```python
import scipy.stats as stats
import math

mu = 60
sigma = 19
n = 44

# 1. Distribution of X
# 2. Distribution of x_bar
sigma_x_bar = sigma / math.sqrt(n)

# 3. P(57 < X < 60)
prob_single = stats.norm.cdf(60, loc=mu, scale=sigma) - stats.norm.cdf(57, loc=mu, scale=sigma)

# 4. P(57 < x_bar < 60)
prob_avg = stats.norm.cdf(60, loc=mu, scale=sigma_x_bar) - stats.norm.cdf(57, loc=mu, scale=sigma_x_bar)

print(f"{mu=}")
print(f"{sigma=}")
print(f"{sigma_x_bar=}")
print(f"{prob_single=}")
print(f"{prob_avg=}")



```

```text
mu=60
sigma=19
sigma_x_bar=2.8643577734887544
prob_single=0.06273012728359711
prob_avg=0.3525321063551667


```

To solve these problems, we use the properties of the normal distribution and the Central Limit Theorem.

### Given Data:

* Population Mean () =  million dollars
* Population Standard Deviation () =  million dollars
* Sample Size () = 

---

### a) What is the distribution of ?

The distribution of the individual production costs () is given as normal.


### b) What is the distribution of ?

According to the Central Limit Theorem, the sampling distribution of the mean () has the same mean as the population, but a smaller standard deviation (known as the standard error).

* Mean of  = 
* Standard Error () = 

---

### c) For a single randomly selected movie, find the probability that this movie's production cost is between 57 and 60 million dollars.

We need to find . We convert these values to z-scores:

* For : 
* For : 

Using the standard normal table or calculator:



**Probability = 0.0627**

---

### d) For the group of 44 movies, find the probability that the average production cost is between 57 and 60 million dollars.

We need to find . Using the standard error calculated in part (b):

* For : 
* For : 


**Probability = 0.3525**

---

### e) For part d), is the assumption of normal necessary?

**No**. Because the sample size () is greater than 30, the Central Limit Theorem guarantees that the distribution of the sample mean will be approximately normal regardless of the shape of the population distribution.