and IT So, what was it?
3) Let's say your experimental results showed that the Placebo Group increased
their IQ score just as much as the Experimental Group. Name 2 possible
explanations for these results.
4) Name 2 ways you could improve on the experiment to determine if eating apples
has any special effect on people?
5) In the above experiment, describe how to conduct it as a double-blind study.

Answer:

The answer is participlating.

I apologize if I am incorrect.

Have a nice day!

~ Myaka O.

Explanation:

because Love encompasses a range of strong and positive emotional and mental states, from the most sublime virtue or good habit, the deepest interpersonal affection, to the simplest pleasure. An example of this range of meanings is that the love of a mother differs from the love of a spouse, which differs from the love of food. Most commonly, love refers to a feeling of strong attraction and emotional attachment

hope its help pa brainiest po

Answer:

...

Explanation:

i don't understand sorry:)

Answer:

15 is correct answer answer

Answer:

the tone is confusion

you can tell because the narrator is quite perplexed by why the Countess was not present in her grandmother's room, and why she had gone out during the sunlight at noon ("shopping hour") , since it is strange for her a married woman like her to do so.

Answer:

Correlation is a statistical technique that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier then start short people. Correlation can tell you just how much of the variations in peoples weights is related to their heights.

The way I will describe the value of a data using a correlation is that; The correlation data gives us the relationship between the factors considered.

Correlation is a term in statistics that is used to test the relationships between quantitative variables or categorical variables. This means that Correlation, is a measure of how things are related.

The study of how variables are correlated is called correlation analysis.

Correlations are very useful because if you can find out what relationship variables have, you can make predictions about future behavior.

Read more about Statistics Correlation at; https://brainly.com/question/4219149

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Answer:

Explanation:

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The mean pressure applied to the driver's head during a crash test is not equal for at least one pair of car types (compact, midsize, and full-size cars).

To test whether the mean pressure applied to the driver's head during a crash test is equal for each type of car (compact, midsize, and full-size), we can use a one-way analysis of variance (ANOVA). ANOVA is used to compare the means of three or more groups.

First, let's define the null and alternative hypotheses:

Null hypothesis (H₀): The mean pressure applied to the driver's head is equal for compact, midsize, and full-size cars.

Alternative hypothesis (H₁): The mean pressure applied to the driver's head is not equal for at least one pair of car types.

We will use a significance level (α) of 5% (0.05).

Given the data:

Compact cars: 643, 655, 702

Midsize cars: 469, 427, 525

Full-size cars: 484, 456, 402

Let's calculate the necessary statistics for the ANOVA table:

Compact cars:

Sample mean (X): 666.67

Sample standard deviation (S): 31.18

Midsize cars:

Sample mean (X): 473.67

Sample standard deviation (S): 49.17

Full-size cars:

Sample mean (X): 447.33

Sample standard deviation (S): 41.68

Now, we can proceed with the ANOVA test and calculate the F-statistic and p-value.

The ANOVA table looks as follows:

Source of Variation | Sum of Squares (SS) | Degrees of Freedom (df) | Mean Square (MS) | F-statistic

----------------------- | --------------------- | --------------------- | ---------------- | --------------------

Between Groups | SS_between | k - 1 | MS_between = SS_between / (k - 1) | F = MS_between / MS_within

Within Groups | SS_within | N - k | MS_within = SS_within / (N - k) |

Total | SS_total | N - 1 |

Where:

- k is the number of groups (car types)

- N is the total number of observations

- SS represents the sum of squares

Let's calculate the ANOVA table using the provided data:

Total number of observations (N): 3 groups x 3 observations per group = 9

Number of groups (k): 3

Between Groups:

Mean of all observations (grand mean):

x-cap = (666.67 + 473.67 + 447.33) / 3 = 529.89

SS_between = (3 × ((666.67 - 529.89)² + (473.67 - 529.89)² + (447.33 - 529.89)²))

Within Groups:

SS_within = (2 × (31.18² + 49.17² + 41.68²))

Total:

SS_total = SS_between + SS_within

Now, we can calculate the degrees of freedom (df) for each source of variation:

df_between = k - 1 = 3 - 1 = 2

df_within = N - k = 9 - 3 = 6

df_total = N - 1 = 9 - 1 = 8

Finally, we can calculate the mean squares:

MS_between = SS_between / df_between = 343,640.67 / 2 = 171,820.335

MS_within = SS_within / df_within = 32,533.99 / 6 = 5,422.33167

We have obtained the actual values for the ANOVA calculations. Now we can proceed to calculate the F-statistic and determine whether to reject or fail to reject the null hypothesis.

Now, let's calculate the F-statistic:

F = MS_between / MS_within = 171,820.335 / 5,422.33167 ≈ 31.73

To determine whether to reject or fail to reject the null hypothesis, we need to compare the calculated F-statistic to the critical F-value from the F-distribution table at the given significance level (α = 0.05). However, we need to determine the critical F-value based on the degrees of freedom for the numerator (df_between) and denominator (df_within).

For df_between = 2 and df_within = 6, the critical F-value at α = 0.05 is approximately 5.14.

Since the calculated F-statistic (31.73) is greater than the critical F-value (5.14), we can reject the null hypothesis.

Therefore, we conclude that the mean pressure applied to the driver's head during a crash test is not equal for at least one pair of car types (compact, midsize, and full-size cars).

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??? please explain further