The phase of inferential statistics which is sometimes considered to be the most crucial because errors in this phase are the most difficult to correct is "data gathering".
<h3>What is
inferential statistics?</h3>
Inferential statistics are frequently employed to compare treatment group differences.
Some characteristics of inferential statistics are-
- Inferential statistics compare treatments groups and make conclusions about the greater population of participants using measures from the experiment's sample of subjects.
- Inferential statistics aids in the development of explanations for a condition or phenomenon.
- It enables you to draw conclusions on extrapolations, which distinguishes it from descriptive statistics, which simply summarize the information that has been measured.
- There are numerous varieties of inferential statistics, each with its own set of research design & sample characteristics.
- To select the correct statistical test of their experiment, researchers should reference the numerous texts about experimental design and statistics.
To know more about the inferential statistics, here
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Answer:
Please find attached the required graph of the inequality representing the temperatures yeast will NOT thrive
Step-by-step explanation:
The given parameters are;
The temperature range, y, in which yeast thrives is 90°F ≤ y ≤ 95°F
Therefore, the temperature range, y', in which yeast will not thrive is 90°F > y and y > 95°F
The graph of the inequality that represents the temperature is therefore given as shown in the attached drawing.
Answer:
The randomization distribution is created under the assumption that H₀: p = 0.1
The randomization distribution will also be centred at 0.1
Step-by-step explanation:
If the distribution was truly random, 1 out of 10 students will choose math as his/her favorite subject.
This means that the randomization will have the null hypothesis saying that the proportion of students who will choose maths as their favourite subject = 0.1
Mathematically, it'll be written as
The null hypothesis is given as
H₀: p = 0.1
And the randomization distribution will be centred at 0.1 too.
The alternative hypothesis will now prove the theory they're looking to see in the question that
Hₐ: p < 0.1
Hope this Helps!!!
Answer:
<em>m=1.7</em>
<em>C=68 gr</em>
Step-by-step explanation:
<u>Function Modeling</u>
We are given a relationship between the carbohydrates used by a professional tennis player during a strenuous workout and the time in minutes as 1.7 grams per minute. Being C the carbohydrates in grams and t the time in minutes, the model is
The slope m of the line is the coefficient of the independent variable, thus m=1.7
The graph of C vs t is shown in the image below.
To find how many carbohydrates the athlete would use in t=40 min, we plug in the value into the equation
Answer:
20 units
Step-by-step explanation:
the perimeter = 7+4+4+5 = 20 units