Please help! I'm leaving school and I have to teach myself this!

Describe how you would test a sample of food to show it contains protein.

harkovskaia [24]

Step-by-step explanation:

Iodine test for starch

Method:

Place one spatula of the food sample on a dish or 1 cm3 if the sample is liquid.

Using a dropper, place a few drops of iodine solution onto the food.

Record any change in the colour of the solution.

Starch is detected using iodine solution. This turns blue-black in the presence of starch.

5 0

3 years ago

Becky graduated with a master degree in Personal Financial Planning. After working two years in a small financial planning firm,

Veronika [31]

Answer:

Becky graduated with a master degree in Personal Financial Planning. After working two years in a small financial planning firm, Becky earns $60,000 annually and saves $10,000 a year. What is her average propensity to consume?a.16.7%b.25.5%c.75.7%d.83.3%e.95.5%14. Which of the following goals is stated in a way that is most useful for developing a financial plan?

Step-by-step explanation:

3 0

3 years ago

Suppose a research firm conducted a survey to determine the mean amount steady smokers spend on cigarettes during a week. A samp

shutvik [7]

Answer:

0.9910 = 99.10% probability that a sample of 170 steady smokers spend between $19 and $21

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution:

Problems of normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .