Microsoft Word - Some_Excel_Functions.doc Economics 120A Homework2 Dr. Maria Cândido Some Excel Functions (some might be useful for Homework 2): (1) Function RAND() This function returns random values...

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there are 16 questions, they are in the word doc. The data is in the excelfile.We need to get the answers by using excel and we need to turn in the excel file as well.


Microsoft Word - Some_Excel_Functions.doc Economics 120A Homework2 Dr. Maria Cândido Some Excel Functions (some might be useful for Homework 2): (1) Function RAND() This function returns random values between 0 and 1. You can use it to randomly select a sample out of a population of values. See homework text for instructions on how to do it. (2) Function COUNTIF Select Insert and then Function and choose COUNTIF. Alternatively, write in a cell =COUNTIF(). This function counts the number of cells within a range that meet a given criteria. = COUNTIF(range, criteria) These are the values you need to input: range is the range of cells from which you want to count cells. criteria is the criterion in the form of a number, expression, or text that defines which cells will be counted. For example, criteria can be expressed as 32, or “32” (count the cells whose value is equal to 32), ">32" (count the cells whose values are higher than 32), "apples" (count the cells whose value is equal to apples), or “>” &A7 (count the cells whose values are higher than the value in cell A7). Example: Suppose you have values in column A, rows 1 to 100. If you want to count the number of cells whose value is equal to 5, use =countif(A1:A100, 5) or = countif(A1:A100, “5”). Alternatively, you can write 5 in a cell (for example cell C1) and then use =countif(A1:A100, C1). If you want to count the number of cells whose values are higher than 5, use =countif(A1:A100, “>5”). Alternatively, you can write 5 in a cell (for example cell C1) and then use =countif(A1:A100, “>”&C1). If you want to count the number of cells whose values are higher than or equal to 5, you can write 5 in a cell (for example cell C1) and then use =countif(A1:A100, “>=”&C1). (3) Function COUNTIFS Select Insert and then Function and choose COUNTIFS. Alternatively, write in a cell = COUNTIFS(). This function applies criteria to cells across multiple ranges and counts the number of times all criteria are met. = COUNTIF(criteria_range1, criteria1, [criteria_range2, criteria2],…) criteria_range1 is the first range in which to evaluate the associated criteria. criteria1 is the criteria in the form of a number, expression, cell reference, or text that define which cells will be counted. For example, criteria can be expressed as 32, or “32” (count the cells whose value is equal to 32), ">32" (count the cells whose values are higher than 32), "apples" (count the cells whose value is equal to apples), or “>” &A7 (count the cells whose values are higher than the value in cell A7. criteria_range2, criteria2, … (Optional) Additional ranges and their associated criteria. Up to 127 range/criteria pairs are allowed. IMPORTANT: Each additional range must have the same number of rows and columns as the criteria_range1 argument. The ranges do not have to be adjacent to each other. Example: If you want the count the number of values in cells A1 through A100 that are between 30 and 40 (not including 30 and 40), you would write =COUNTIFS(A1:A100,”>” 30,A1:A100,”<=”40) if="" you="" want="" the="" count="" the="" number="" of="" values="" in="" cells="" a1="" through="" a100="" that="" are="" between="" the="" value="" specified="" in="" cell="" c1="" (inclusive)="" and="" the="" value="" specified="" in="" cell="" d1,="" you="" would="" write="COUNTIFS(A1:A100," ”="">=” &C1 , A1:A100 , ”<”&d1) economics="" 120a="" homework2="" dr.="" maria="" cândido="" (4)="" function="" averageif="" select="" insert="" and="" then="" function="" and="" choose="" averageif.="" alternatively,="" write="" in="" a="" cell="AVERAGEIF()." this="" function="" returns="" the="" average="" of="" all="" the="" cells="" in="" a="" range="" that="" meet="" a="" given="" criteria.="AVERAGEIF(range," criteria,="" average_range)="" range="" is="" one="" or="" more="" cells="" to="" average,="" including="" numbers="" or="" names,="" arrays,="" or="" references="" that="" contain="" numbers.="" criteria="" is="" the="" criterion="" in="" the="" form="" of="" a="" number,="" expression,="" cell="" reference,="" or="" text="" that="" defines="" which="" cells="" are="" averaged.="" for="" example,="" criteria="" can="" be="" expressed="" as="" 32,="" "32",="" "="">32", "apples", or B4 “>=”&B4. average_range is the actual set of cells to average. If omitted, range is used. Example: Suppose you have profits in cells C1 through C100. In cells A1 through A100, you have the year those profits were made, and in cells B1 through B100, you have the quarter (1, 2 , 3, or 4) those profits were accrued. If you want to find the average profit in the first quarter of the years, you would write =AVERAGEIF(B1:B100, 1, C1:C100) (5) Function NORM.S.INV (or NORMSINV in earlier versions of Excel) Select Insert and then Function and choose NORM.S.INV. Alternatively, write in a cell = NORM.S.INV(). This function returns the critical values of the STANDARD NORMAL cumulative distribution. = NORM.S.INV(probability) probability is a probability corresponding to the normal distribution, a number between 0 and 1. Example: Z follows a STANDARD normal distribution. If you want to know the value of Z to the LEFT of which 5% of the distribution lies, then you would write: = NORM.S.INV(0.05) If you want to know the value of Z to the RIGHT of which 5% of the distribution lies, then you would write: = NORM.S.INV(0.95) (6) T.INV function Select Insert and then Function and choose T.INV. Alternatively, write in a cell =T.INV(). This function returns critical values of the Student's t-distribution as a function of the probability and the degrees of freedom. =T.INV(probability,deg_freedom) probability is the probability associated with the Student's t-distribution. deg_freedom is the number of degrees of freedom with which to characterize the distribution. Example: W is a random variable that follows the Student t-distribution with 30 degrees of freedom. If you want to know the value of W, w, to the LEFT of which lies 0.90 of the distribution, you would write =T.INV(0.90, 30). The function would return that value w, such that Pr(W < w)="0.90." economics="" 120a="" homework2="" dr.="" maria="" cândido="" (7)="" function="" t.inv.2t="" select="" insert="" and="" then="" function="" and="" choose="" t.inv.2t="" alternatively,="" write="" in="" a="" cell="T.INV.2T()." this="" function="" returns="" critical="" values="" of="" the="" student's="" t-distribution="" as="" a="" function="" of="" the="" probability="" and="" the="" degrees="" of="" freedom.="T.INV.2T(probability," degrees_freedom)="" probability="" is="" the="" probability="" associated="" with="" the="" two-tailed="" student's="" t-distribution.="" degrees_freedom="" is="" the="" number="" of="" degrees="" of="" freedom="" with="" which="" to="" characterize="" the="" distribution.="" example:="" w="" is="" a="" random="" variable="" that="" follows="" the="" student="" t-distribution="" with="" 30="" degrees="" of="" freedom.="T.INV.2T(0.10," 30)="" returns="" that="" value="" w,="" such="" that="" p(|w|=""> w) = 0.10 where P(|W| > w) = P(W < -w="" or="" w=""> w). WARNING: These last two functions were created for the newer versions of Excel. I have Excel 2007 for Mac and as such, I don’t have these two last functions. I have a function called TINV, which does the same as T.INV.2T. Make sure you check your version of Excel for an explanation of these types of functions. (8) Function NORM.INV (or NORMINV in earlier versions of Excel) Select Insert and then Function and choose NORM.INV. Alternatively, write in a cell = NORM.INV(). This function returns critical values of the normal cumulative distribution for the specified mean and standard deviation. = NORM.INV(probability, mean, standard_dev) probability is a probability corresponding to the normal distribution, a number between 0 and 1. Mean is the mean of the distribution. Standard_dev is the standard deviation of the distribution. Example: Suppose you know that X follows a normal distribution with mean 65 and standard deviation 17. If you want to know the value of X to the LEFT of which 20% of the distribution is, then you would write: = NORM.INV(0.20, 65, 17) If you want to know the value of X to the RIGHT of which 20% of the distribution is, then you can write: = NORM.INV(0.80, 65, 17) (9) Function NORM.DIST (or NORMDIST in earlier versions) Select Insert and then Function and choose NORM.DIST. Alternatively, write in a cell = NORM.DIST(). This function returns the normal distribution for the specified mean and standard deviation. = NORM.DIST(x, mean, standard_dev, cumulative) x is the value for which you want the distribution. Mean is the mean of the distribution. Standard_dev is the standard deviation of the distribution. Cumulative is a logical value that determines the form of the function. If cumulative is TRUE, NORMDIST returns the cumulative distribution function, i.e., it returns the probability mass to the LEFT of x. Notice that while the normal distribution table in your textbook gives you the area to the right of each specific number; the Excel normdist(x, mean, standard_dev, TRUE) will give you the area to the left of x (Prob(X < x)). if cumulative is false, then it returns the probability density function. example: suppose you know that x follows a normal distribution with mean 65 and standard deviation 17. if you want to know the probability that x is lower than 80, you would write: = norm.dist(80, 65, 17, true) if you want the probability that x is higher than 80 you would write = 1- norm.dist(80, 65, 17, true) if you want the probability that x is between than 60 and 80 you would write = norm.dist(80, 75, 67, true) – norm.dist(60,65, 17, true) economics 120a homework2 dr. maria cândido (10) function t.dist (or t x)).="" if="" cumulative="" is="" false,="" then="" it="" returns="" the="" probability="" density="" function.="" example:="" suppose="" you="" know="" that="" x="" follows="" a="" normal="" distribution="" with="" mean="" 65="" and="" standard="" deviation="" 17.="" if="" you="" want="" to="" know="" the="" probability="" that="" x="" is="" lower="" than="" 80,="" you="" would="" write:="NORM.DIST(80," 65,="" 17,="" true)="" if="" you="" want="" the="" probability="" that="" x="" is="" higher="" than="" 80="" you="" would="" write="1-" norm.dist(80,="" 65,="" 17,="" true)="" if="" you="" want="" the="" probability="" that="" x="" is="" between="" than="" 60="" and="" 80="" you="" would="" write="NORM.DIST(80," 75,="" 67,="" true)="" –="" norm.dist(60,65,="" 17,="" true)="" economics="" 120a="" homework2="" dr.="" maria="" cândido="" (10)="" function="" t.dist="" (or="">
Answered Same DayMar 15, 2021

Answer To: Microsoft Word - Some_Excel_Functions.doc Economics 120A Homework2 Dr. Maria Cândido Some Excel...

Pooja answered on Mar 16 2021
145 Votes
Scores
    Population    Levels    rand()        Sample    Levels            Population
    66.5    SR    0.0002282986        76.5    JR                Average        71.4
    76.5    JR    0.0014659962        50.5    JR                Standard Deviation        16.32
    50.5    JR    0.0028403832        92    JR                Sample size needed         144    so that sampling error = 3.5        n = z(a/2)^2*sd^2/ME^2
    92    JR    0.0040031464        65    JR
    78.5    SR    0.0040777189        61.5    JR
    65    JR    0.0055843691        77    JR            Sample taken
    61.5    JR    0.0057249483        74.5    JR                Sample size        121
    77    JR    0.0058512826        68    JR                Sample Average        71.7
    89.5    SR    0.0069892775        97    JR                Sample Standard Deviation (s)        15.2
    87.5    SR    0.0071194028        54.5    JR                True
    74.5    JR    0.0072059395        65.5    JR
    68    JR    0.0078300333        71.5    JR            Confidence Interval for the Population Mean (Critical Values from Normal Distribution)
    97    JR    0.0081897043        40.5    JR                Confidence Level        0.99
    54.5    JR    0.0082277438        95.5    JR                Standard Error of the Sample Average        0.36
    36    SR    0.0088767996        42    JR                Sampling Error        0.93
    83.5    SR    0.0090076173        69    JR                Lower Bound        70.5
    65.5    JR    0.0096662802        54.5    JR                Upper Bound        72.4
    71.5    JR    0.0105702748        83    JR
    40.5    JR    0.0106277908        73    JR
    84    SR    0.010743306        62.5    JR            Confidence Interval for the Population Mean (Critical Values from Sttudent-t Distribution)
    84    SR    0.0115618602        48    JR                Standard Error of the Sample Average        1.38
    95.5    JR    0.0119455087        74.5    JR                Sampling Error        3.61
    42    JR    0.0121326614        54.5    JR                Lower Bound        68.1
    69    JR    0.013091262        49.5    JR                Upper Bound        75.4
    54.5    JR    0.0150068959        69.5    JR                Those two 99% confidence intervals are similar to each other
    95.5    SR    0.0158407543        91.5    JR                99% confidence interval include the population mean μ as 71.4 is contained in this confidence interval.
    83    JR    0.0163562558        61    JR            By Student Level
    91    SR    0.0166464662        67.5    JR                Sample Average    Count    Variance    Standard Dev
    73    JR    0.0175323276        72    JR            SR    73.3    40    339.6    18.4
    62.5    JR    0.0192426351        57    JR            JR    71.0    81    178.8    13.4
    35.5    SR    0.0193589444        87.5    JR
    86.5    SR    0.0201802126        65.5    JR                Difference in Sample Averages (SR - JR)         2.3
    90.5    SR    0.0202694161        64    JR                Estimated Variance of Difference in Sample Averages        10.70
    48    JR    0.0204829016        59    JR                Standard Error of the Difference of Sample Averages        3.27
    67    SR    0.0206912489        63    JR                Confidence Level        0.90
    81.5    SR    0.0214292708        76    JR                Sampling error (use critical values from normal)        5.38
    74.5    JR    0.0219605858        90    JR
    54.5    JR    0.022877223        77.5    JR
    49.5    JR    0.022942455        58    JR            Confidence Interval for the Difference in Means (between Seniors and Juniors)
    69.5    JR    0.0236072596        81    JR                Lower Bound        -3.1
    91.5    JR    0.02384173        85.5    JR                Upper Bound        7.7
    69.5    SR    0.0243272397        73    JR
    84.5    SR    0.0248017834        65    JR                Since confidence interval contains zero, I fail to reject the hypothesis that the mean score for junior students is the same as the mean score for senior...
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