Question
Answered step-by-step
toriermcvaych85
1.Respond to this post below. One type of tendency is Central…
1.Respond to this post below.
One type of tendency is Central Tendency. This is a statistical measure that identifies a single measure representing the distribution in it’s entirety. Mean, median, and mode are the three principal measures. The correct measure of Central Tendency really depends on the kind of data and what the researcher wants to reflect from the data. The type of measure to be used, depends on if the variable is nominal, ordinal, symmetrical, or skewed. SPSS (Statistical Package for Social Sciences) is a computer program used for statistics. SPSS obeys certain rules: go to menu, analyze submenu, descriptive statistics submenu, and the frequencies option. Finally, bring up all measures and check the boxes that are necessary. A researcher should find Central Tendency of probability distribution to allow him to arrange the data in increasing order, or from the smallest to the largest.
2.Respond to this post below.
The three measures of central tendency are mean, median, and mode. Mean is the most often used in central tendency because it will use all the values in the data set to get the average. The mode is usually the most common score or number. Lastly, the median is the middle point of the numeric sentence or score. During an experiment, it is important for a researcher to find the central tendency of a probability distribution because it informs the researcher more about the data that is being collected and used to differentiate between variables.
3.Respond to the post below.
The sum of squares, variance, and standard deviation are all descriptive measures of variability. Variance is the average squared distance from the mean. Standard deviation is the average distance of a score to the mean and the sum of squares is the total of the squared deviation scores.
The difference between sample statistics and population parameters is that a parameter is describing an entire population whereas a statistic is describing only a sample of that population. Knowing the difference is essential to make proper calculations and in understanding the data that has been collected.
4. Respond to the post below.
The measure of sum of squares (SS) is the sum of squared deviation scores (Gravetter, et.al., 2021). Variance is the mean square deviation and standard deviation is the square root of the variance in which it measures the distance from the mean. This is the point that measures the variability between each other (Gravetter, et.al., 2021). It is essential to understand the difference between sample statistics and population parameters because it describes the population and calculates the sample data.
5. Respond to the post below.
“In the event of tossing a coin, it is difficult or even impossible to predict the result. For sure, there are two possible outcomes: getting a head or a tail. However, tossing a fair coin is an independent event, which means that the probability of getting a head, or a tail is equal, 1/2 and remains the same (Gravetter, et al, 2021).” I think my friend is wrong, because the outcome stays the same, and does not depend on the number of times I toss the coin. On a fair toss, I will get either heads or tails. Since the coin is fair, regardless of the number of coin tosses-the probability will remain equal to get a head or tail. Still, on an infinite number of times tossing a fair coin, I think the probability of getting heads or tails will remain the same.
6. Respond to the post below.
In this scenario, the coin that is being flipped is called a “fair coin.” The definition of a fair coin is that it is just as likely to land on heads as it is to land on tails (GeeksforGeeks, 2021). This makes the chance of it landing on one over the other is 50/50 chance. With this information in mind, my friend’s claim would be incorrect because they assume that I would have a higher chance of landing on heads than on tails, but I have an equal chance of landing on either side. If I were to flip a coin constantly an infinite number of times, I would expect the coin to land on both sides an equal number of times.
7. Respond to the post below.
A z score is called the standardized distribution. It is used to identify locations of each score in a distribution. It consists of simple values that serve for the mean and standard deviation but doesn’t change any location while the distribution (Gravetter, et al, 2021). The information that is being provided by the sign scores the higher or lower than the mean. The numerical value is being provided by the multiple scores that are above the mean and the scores below the mean.
8. Respond to the post below.
Hello Profesor and class, the standardized distribution also known as standard normal distribution is made by converting the data values of a distribution into Z scores. In a standardized distribution, the mean will be 0 and the standard deviation is 1. The standardized distribution is made by converting the data values of a distribution into Z scores. Sign (+/-) for a Z score represents that the data value is either below or above the mean. The numerical value of the Z-score speaks to the data value is how many standard deviations are above or below the mean. This tells us how many standard deviations above or below the mean the score is. After they are changed to z scores the new X values can be created based on the new mean and standard deviation.
