all questions. Answer "True" or "False" for the questions below. 1) The range is a measure of central tendency. 2) The mean of a data set is also known as the average value. 3) The median of a data set is found by adding all the values in the data set and then dividing the result by the number of observations. 4) The central tendency is where the majority of the data points can be found in a histogram. 5) A weighted mean allows you to assign more weight to certain values and less weight to others. 6) When calculating a weighted mean, the weights need to sum to 1.0. 7) The mean is a convenient measurement for summarizing a data set with a single value. 8) When you rely on the mean to summarize many data values, you lose important information about the original data. 9) Outliers are extreme values above or below the mean that require special consideration. 10) One disadvantage of using the median is that it can be heavily influenced by outliers. 11) Because outliers are extreme values that could distort the analysis, they should always be eliminated from the data set. 12) When the index point, i, is not a whole number, round i up to the next highest whole number to find the position of the median in a sorted data set. 13) When there is an even number of data values, the median is always the middle value in the data set. 14) The mode is the most useful measure of central tendency for a data set. 15) When there are an odd number of data values, the median is halfway between the two middle values in the data set. 16) There can be more than one mode in a data set. 17) Some data sets may not have a mode. 18) Measures of variability show how much spread is in your data. 19) The unit of measure for the variance is the same as the unit of measure for the original data. 20) When all of the data values in the sample are the same, the sample standard deviation equals 1.0. 21) The standard deviation of a data set can never be negative. 22) Using n - 1 in the denominator for the sample variance is a much better estimate of the true population variance when compared to dividing by n. 23) The sample variance is a biased estimator of the population variance because we would expect the average of many sample variances taken from a population would be equal to the true population variance. 24) In a quality control setting, the sample mean provides enough evidence to determine if a process is working properly. 25) When sample means of two data sets are very different, comparing sample standard deviations can be misleading. 26) In quality control settings, businesses prefer a larger standard deviation, which is an indication of more consistency in the process. 27) When comparing two samples, the smaller coefficient of variation indicates more consistency (or less variability) within the sample or population. 28) The average month end closing stock price for Company A over the past year is $34.57 with a standard deviation of $4.65. The average month end closing stock price for Company B over the same period is $26.15 with a standard deviation of $7.45. Based on this data, we can conclude that the stock price for Company A is more consistent when compared to the stock price for Company B. 29) The average monthly rent for a two-bedroom apartment in City A is $820 with a standard deviation of $86. The average monthly rent for a two-bedroom apartment in City B is $790 with a standard deviation of $75. Based on this data, we can conclude that the rent for two-bedroom apartments in City A is more consistent when compared to City B. 30) The z-score has no units even though the original values will normally be expressed in units such as dollars, years, pounds, or calories. 31) Data values that have z-scores above +1.0 or below -1.0 are categorized as outliers. 32) According to the empirical rule, we would expect approximately 68, 95, and 99.7 percent of the values to fall within one, two, and three standard deviations above and below the mean respectively for all distributions. 33) Chebyshev's Theorem only applies to bell-shaped curves that are relatively symmetrical. 34) When calculating the sample mean of grouped data, the value represents an estimate of the true sample mean. 35) The sample standard deviation for grouped data provides an exact value for the sample standard deviation. 36) Measures of relative variation compare the position of one value in relation to other values in the data set. 37) Percentiles measure the approximate percentage of values in the data set that are below the value of interest. 38) The definition of the pth percentile (where p is any number between 1 and 100) is that at least p percent of the observations will fall below the value of interest. 39) Steve's grade on a recent statistics exam was an 85 on a scale of 0-100. Based on this information, we can conclude that Steve's grade was in the 85th percentile in his class. 40) The percentile rank identifies the percentile of a particular value within a set of data. 41) Percentile ranks are very useful when comparing values from two different data sets. 42) A scatter plot is a graphical display showing the relative position of the three quartiles as a box on a number line along with the minimum and maximum values in the data set and any outliers. 43) The five-number summary consists of a distribution's minimum value, the values for the first, second, and third quartiles, and maximum value. 44) Outliers are not included in a five-number summary. 45) A box-and-whisker plot displays the mean of the data set. 46) The sample covariance measures the direction of the linear relationship between two variables. 47) The sample covariance measures the strength of the linear relationship between two variables. 48) A negative sample covariance indicates that as one variable increases in value, the second variable tends to decrease. 49) The sample correlation coefficient measures the strength and direction of the linear relationship between two variables. 50) The sample correlation coefficient can be a positive value, a negative value, or zero