In: Science

Submitted By khchoi

Words 671

Pages 3

Words 671

Pages 3

Weighted Mean =SUMPRODUCT(weights, values) / n

Where n = SUM(weights)

Median =MEDIAN()

Mode =MODE()

Range =MAX()-MIN()

Class Width =range / number of classes All Versions of Excel Excel 2010

Variance Sample: =VAR() =VAR.S() Population: =VARP() =VAR.P()

Standard Deviation (SD) Sample: =STDEV() =STDEV.S() Population: =STDEVP() =STDEV.P() Or,=SQRT(variance) Or, =variance^0.5

Skewness =3*(AVERAGE() - MEDIAN()) / SD

Coefficient of Variation =SD / AVERAGE() * 100

Em. R 1)68 2)95 3)99.7 lower value : higher value

Lower = mean – stdev (2)(3)

Higher= mean+stdev (2)(3)

Grouped Data Weighted Mean:

=SUMPRODUCT(frequencies,class midpoints)/n

Where n = SUM(class frequencies)

Median: class that has middle frequency: =(n+1)/2

Variance:

=SUMPRODUCT(frequencies, deviations) / (n-1)

Where deviations = (midpoint - mean)^2

Population is same except divide by n.

SD: =SQRT(variance)

Chebyshev = 1 - 1 / k^2

Where k = number of standard deviations

Percentile i = percentile/100 * n

Round up if decimal; average if whole.

N= # of data values, Whole # = average it out

IQR, Outliers = Q3 - Q1 Upper Outlier =Q3 + 1.5 * IQR (75) Lower Outlier =Q1 − 1.5 * IQR (25)

Probability P(A)

P(A or B) = A / Total

= P(A) + P(B) - P(Both) P(A and B)

P(A and B)

P(A | B)

P(A | B) = P(A) * P(B)

= P(A) * P(B|A)

= P(A and B) / P(B)

= P(A|B)*P(B)

P(A|B)P(B)+P(A|Bc)P(Bc)

= P(B|A)*P(A) / P(B) Independent

Dependent

Conditional

Bayes' Rule

Permutation =PERMUT(n,r) (N=count of data values)

Combination =COMBIN(n,r) (R=size of smaller group taken from N) Discrete Distributions Expected Value: =SUMPRODUCT(x,P(x))

Variance: =SUM(x^2 * p) - (expected value^2)

SD: =SQRT(discrete variance)

Binomial Distributions

Mean: =n*p

Variance: =n*p*(1 - p)

SD: =SQRT(variance)

Probability:…...

...• o Question o Answer o Side 3 • o Budgeted At Completion (BAC) o How much was originally planned for this project to cost. No one formula exists. Is derived by looking at the total budgeted cost for the project. o No one formula exists. Is derived by looking at the total budgeted cost for the project. • o Planned Value (aka Budgeted Cost of Work Scheduled) (PV or BCWS) o How much work should have been completed at a point in time based on the plan. Derived by measuring planned work completed at a point in time. Planned % Complete x BAC o Planned % Complete x BAC • o Earned Value (aka Budgeted Cost of Work Performed) (EV or BCWP) o How much work was actually completed during a given period of time. Derived by measuring actual work completed at a point in the schedule. EV = Actual % Complete x BAC o EV = Actual % Complete x BAC • o Actual Cost (aka Actual Cost of Work Performed) (AC or ACWP) o The money spent during a given period of time. Sum of the costs for the given period of time. o Sum of the costs for the given period of time. • o Cost Variance (CV) o The difference between what we expected to spend and what was actually spent. CV = EV-AC o CV = EV-AC • o Schedule Variance (SV) o The difference between where we planned to be in the schedule and where we are in the schedule SV = EV-PV o SV = EV-PV • o Cost Performance Index (CPI) o The rate at which the project performance is meeting cost expectations during a......

Words: 394 - Pages: 2

.... . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Limitations and Common Misinterpretations of Hypothesis Testing . . . . . . . . . . 1 1 6 10 15 17 Stat 3011 Chapter 9 CHAPTER 9: HYPOTHESIS TESTS Motivating Example A diet pill company advertises that at least 75% of its customers lose 10 pounds or more within 2 weeks. You suspect the company of falsely advertising the beneﬁts of taking their pills. Suppose you take a sample of 100 product users and ﬁnd that only 5% have lost at least 10 pounds. Is this enough to prove your claim? What about if 72% had lost at least 10 pounds? Goal: 9.1 Elements of a Hypothesis Test 1. Assumptions 2. Hypotheses Each hypothesis test has two hypotheses about the population: Null Hypothesis (H0 ): Alternative Hypothesis (Ha ): 1 Stat 3011 Chapter 9 Diet Pill Example: Let p = true proportion of diet pill customers that lose at least 10 pounds. State the null and alternative hypotheses for the diet pill example. 3. Test Statistic Deﬁnition: Test Statistic A test statistic is a measure of how compatible the data is with the null hypothesis. The larger the test statistic, the less compatible the data is with the null hypothesis. Most test statistics we will see have the following form: What does a large value of |T | reﬂect? NOTE: 2 Stat 3011 Chapter 9 4. p-value The p-value helps us to interpret the test statistic. Deﬁnition: p-value Assume H0 is true. Then the p-value is the probability...

Words: 2046 - Pages: 9

...Factors that Affect Credit Score? FICO makes the formulas and programs for all credit reporting agencies. The names of formulas and actual procedures are different from agency to agency, the basic factors affecting credit score are however the same and the basic formula and its constituents remain the same. The three different models for credit scoring by FICO include, BEACON score used by Equifax, Experian/Fair Isaac Risk Model used by Experian and EMPIRICA used by TransUnion. The companies do not disclose the exact formulas but as per FICO resources, the following are the things that make up a credit score and also tend to affect the score. * Payment History (35%): The payment history basically consists of all your past accounts and the regularity with which payments have been made. A bad and irregular payment history causes the score to drop down. * Amounts Owed (30%): The total amount of debts owed to other lenders is also an important consideration in the score calculation. The standard equation is, more the amounts owed, less is the credit score. Hence keep the credit history and current liabilities to the bare minimum. * Length of Credit History (15%): The length of the credit history is also considered. Rule of the thumb is that longer the history, lesser is the score. Thus avoid unnecessary borrowings and keep them to the bare minimum. * New Credit (10%): New credit consists of the newly borrowed loans or newly taken up credit cards. Keeping it small always helps,......

Words: 834 - Pages: 4

...STAT 302 – Statistical Methods Lecture 8 Dr. Avishek Chakraborty Visiting Assistant Professor Department of Statistics Texas A&M University Using sample data to draw a conclusion about a population • Statistical inference provides methods for drawing conclusions about a population from sample data. • Two key methods of statistical inference: o o Confidence intervals Hypothesis tests (a.k.a., tests of significance) Hypothesis Testing: Evaluating the effectiveness of new machinery at the Bloggs Chemical Plant • Before the installation of new machinery, long historical records revealed that the daily yield of fertilizer produced by the Bloggs Chemical Plant had a mean μ = 880 tons and a standard deviation σ = 21 tons. Some new machinery is being evaluated with the aim of increasing the daily mean yield without changing the population standard deviation σ. Hypothesis Testing: Evaluating the effectiveness of new machinery at the Bloggs Chemical Plant Null hypotheses • The claim tested by a statistical test is called the null hypothesis. The test is designed to assess the strength of the evidence against the null hypothesis. Usually the null hypothesis is a statement of “no effect” or “no difference”, that is, a statement of the status quo. Alternative hypotheses • The claim about the population that we are trying to find evidence for is the alternative hypothesis. The alternative hypothesis is one-sided if it states that a parameter is larger than or...

Words: 921 - Pages: 4

...be larger than the difference between 500 and the number of heads in the second trial. (b) to get exactly 500 heads in the second trial. c. the chance error expressed as a percentage of the number of tosses to be smaller in the first trial than in the second trial. c. all of the above statements. c. none of the above statements. 2. A box contains 99 zeros and 1 one. If we make draws from this box with replacement, a. the probability histogram for the sum of the draws ( when put in standard units) will follow the normal curve after a small number of draws. a. then the probability histogram for the numbers in the box is close to the normal curve if the number of draws is very large. a. we can use the binomial formula to compute the chance of getting exactly 3 ones in 10 draws. b. both (a) and (c) are true. a. both (a) and (b) are true. 3. When thinking about sample surveys we should remember, a. a parameter is a numerical fact about a sample, subject to chance variation. b. the researcher uses the population to compute a statistic. c. that the parameter may be subject to sampling bias. d. simple random sampling means drawing the subjects at random without replacement. e. both (c) and (d). 4. Suppose we were interested in the percentage of A’s given in two different classes, Physics 101 and Psychology 101. So, we conduct a simple random sample of 30 students from each class. The physics class has 300 students......

Words: 1529 - Pages: 7

...To Write a Chemical Formula in OWL Enclose subscripts with underscores _. Enclose superscripts with carats ^. The underscore key is next to the number zero on the keyboard. The carat key is the number six on the keyboard. H_2_O = H2O Cr^3+^ = Cr3+ Combined: SO_4_^2−^ = SO42− Ions Unit Charge Ions Write the number first and then the charge. Do not include the number one in unit charge ions. N^3−^ = N3− Ca^2+^ = Ca2+ Na^+^ = Na+ Cl^−^ = Cl− Using the Chemical Formula Input The chemical formula input box displays the superscripts and subscripts as you enter the formula. There are 3 ways to use the input box. • Keyboard: Use the keyboard to enter underscores and carats on your own. • Buttons after: Enter the formula without underscores or carats, then highlight each superscript and/or subscript, click the appropriate subscript or superscript button, and the underscores or carats will be filled in automatically. • Button during: Use the subscript or superscript buttons to enter the underscores and carats while you type the formula. To Write a Chemical Formula in OWL Enclose subscripts with underscores _. Enclose superscripts with carats ^. The underscore key is next to the number zero on the keyboard. The carat key is the number six on the keyboard. H_2_O = H2O Cr^3+^ = Cr3+ Combined: SO_4_^2−^ = SO42− Ions Unit Charge Ions Write the number first and then the charge. Do not include the number one in unit charge ions. N^3−^ = N3− Ca^2+^ =......

Words: 264 - Pages: 2

... |6084 |100 |780 | |8 |99 |8 |9801 |64 |792 | |9 |49 |29 |2401 |841 |1421 | |10 |78 |11 |6084 |121 |858 | |Total |724 |154 |54226 |2856 |10289 | a) Write the Population Regression Equation. b) What is the estimated regression equation? c) Calculate the linear relationship between “the percentage of the refund spent within three months of their receipt” and “annual family income” (calculation using formula, use printout given above), Explain (0, (1, also provide the units of slope and y-intercept. Does (0, (1 make sense? d) If the Family income is $ 80,000, what will the % refund spent (Provide Units), do you have any concerns, explain? e) If the Family income is $ 180,000, what will the % refund spent (Provide Units), do you have any concerns, explain? f) For what range of x-value is the regression equation valid? g) What is extrapolation? 2. Input question one data into Minitab and get the printout, the printout should look similar to this. Regression Analysis: Y versus X The regression equation is Y = _____ - 0.476 X Predictor Coef SE Coef T P Constant ______ 5.297 9.41 ......

Words: 1807 - Pages: 8

...Formulas: 4 Total Cost 4 Contribution Margin 4 Unit Contribution 4 Total Contribution 4 Profit 4 Channel Margins 5 Margins (in %) – Based on Price 5 Mark-Ups (in %) – Based on Costs 5 Example: 5 Moving Up & Down the Value Chain 6 Move “Up” Chain 6 Move “Down” Chain 6 Breakeven Analysis 6 BE (units) 6 BE (dollars of sales) 7 Market Share 7 Dollar Share 7 Unit Share 7 BE MS (Dollars) 7 BE MS (Units) 7 Cannibalization 7 Total Contribution (NP) 7 Net Present Value: Today’s $ v. Next Year’s $ 8 Customer Lifetime Value (CLV) 8 Mkt Strat I: Strategy Formulation, Market Assessment Tools (Frameworks), Porter’s Generic Strategies 9 Dolan’s 5 Cs 9 Porter’s 5 Forces Model 10 BCG Matrix 10 Marketing Strategy II: Segmentation and Positioning 11 S-T-P 11 Consumer Segmentation Variables: 11 Business Segmentation Variables: 11 Characteristics of Effective Segmentation 11 Bases for Segmentation Evaluation 12 Targeting the Markets 12 Pricing 12 Top 3 of 5 Deadly Pricing Sins 12 8 Steps to Better Pricing Decisions 12 Value-Based Pricing 13 Marketing Research 14 Steps in Marketing Research 14 Reliability v. Validity 14 Marketing Channels 15 Value Adding Roles of Intermediaries 15 Channel Conflict and Efficiency 15 Salesforce Management 15 McMurry’s sales representative types: 16 Motivating the Salesforce 16 Simple Salesforce Structures 16 Salesforce Size 17 Brand Management 17 Brand......

Words: 2241 - Pages: 9

...Evaluation of Financial Policy GBA 546 Formula Sheet Prepared by P. Sarmas Cash Flow from Assets = Cash Flow to Creditors + Cash Flow to Stockholders Operating Cash Flow Interest Paid Dividend Paid - Net Working Capital - Net New Borrowing - Net New Equity - Net Capital Spending Cash Flow to Creditors Cash Flow to Stockholders Cash Flow from Assets EBIT Ending Net Fixed Assets + Depreciation - Beginning Net Fixed Assets - Taxes + Depreciation . Operating Cash Flow Net Capital Spending Ending Net Working Capital (CA – CL) - Beginning Net Working Capital (CA-CL) Change in Net Working Capital Ending L.T. Debt Ending Equity - Beginning L.T. Debt - Beginning Equity Net New Borrowing - Addition to Retained Earnings Net New Equity Dividend Payout Ratio = Dividends Net Income ROADuPont = Profit Margin * Total Assets t/o ROEDuPont = Profit Margin * Total Assets t/o * Equity Multiplier Earnings Retention Ratio = b = 1 – Dividend Payout Ratio = 1- DIV/NI (1+R) = (1+r)*(1+h) Operating Cycle = Inventory Period + Accounts Receivable Period Cash Cycle = Operating Cycle – Accounts Payable Period Operating Cash Flow = EBIT + Depreciation – Taxes Operating Cash Flow = (Sales – OC – Depreciation)*(1-T) + Depreciation......

Words: 311 - Pages: 2

..., Pn =Pno (ex: P1 = ¾) Ha: the probability distribution is different than stated in the null hypothesis Or Ho: the _____________________are distributed as follows__________(in percent form) Ha: the distribution is different than stated in the null hypothesis Or more common hypothesis: Ho: usually given in ratios: the ratio of _________is ___:____:_____. Ha: the ratio of ___________ is not ___:____:_____. D.F: number or cells -1 Note: Pio = Hypothesized probabilities and the probability must be between 0 and 1 inclusive Note: n = total observed and p = probability from the ratio Note: all E ≥ 5 if its not you have to collapse the cells until it is. Then recalculate D.F and x^2 Note: if D.F. = 1 then use x^2 corrected formula to calculate x^2 Note: the lowest number of cells you can have is 2 = X^2 corrected F- Distribution: The F - Distribution: named after sir Ronald Fisher. The F - Distribution are a family of curves, each of which is specified by two degrees of freedom: v1, v2 . the F – distribution is used in the testing of two variances where σ1 and σ2 are the variances of two independent normal populations, form which the samples are taken from, Ho: σ1 = σ2. Note: form now on degree of freedom is notated as v (nu) F – Distribution test for two variances: One tailed test: Ho: σ12= σ22 Ha: σ1 2 > σ22 (we choose what pop 1 so that Ha is always in this way, (we always define things so that s 12 is expected to be bigger then...

Words: 1058 - Pages: 5

...has unusually large or unusually small values will also be determined using the same statistics (Anderson, et al., 2011). An evaluation of these descriptive statistics and the relationship between the total gross sales will be the focus of this critical thinking exercise. Good introduction. The first data set includes opening day gross revenues. The median opening day gross was .39 which means that half of the movies in this data set were less than .39 and the rest were more than .39. The median is the middle of an ordered score of an odd number of data or half way between the even two numbers. The mean was 9.38 and the standard deviation was 18.875 based on 100 movies (Expert, 2011). Simple mean is calculated with the follow formula: x=Ex1/n.The opening day variance is .03 to 108.44 ($ millions) equaling 108.43. Therefore with a median of .39 and a mean of 9.38 indicates that there are many movies on opening day that are not making money and a few are making much money. (Expert, 2011) The second set of data is the total gross revenue column. The data width or range is from .03 to 380.176 equaling 380.18 which is a large variance. The median total gross sales were 5.85% which is much higher than the opening day sales. The mean is 33.04 and the standard deviation is 63.165 based on the same 100 films. The histogram illustrates this data information in a visual content below. This indicates that there are outliers specifically on the high end......

Words: 330 - Pages: 2

...MAR 4231 = Financial Formulas Note: When calculating the financials, please round to four decimal places. For example: 1.7658643983 = 1.7659 (four decimal places) 0.4322222222 = 0.4322 (four decimal places) Asset turnover = Net sales Total assets Cost complement = is the relationship of cost to retail value of merchandise available for sale Total cost valuation Total retail valuation Cost of goods sold = Cost of merchandise available for sale – cost value of ending inventory Ending retail book value of inventory = on paper, how much is your inventory worth (at retail) = Merchandise available for sale – Sales – Deductions Financial Leverage = Total assets Net worth Gross Profit = Sales – Cost of Goods Sold Net Profit = Gross Profit – Operating Expenses Net Profit Margin = Net profit after taxes Net sales Profit & Loss Statement = Sales – less cost of goods sold = gross profit Return of Assets = Net profit margin x asset turnover Return on Net worth = Net profit margin x Asset turnover x Financial leverage Stock Shortages = how much inventory was stolen/lost? Ending retail book value of inventory – physical inventory at retail Total merchandise available (at cost) = Beginning monthly inventory + Net purchases + transportation charges Total merchandise available (at retail) = Beginning monthly inventory + Net purchases Adjusted ending retail book value of inventory = adjusted the value of the retail book value of......

Words: 369 - Pages: 2

...FORMULAS CHAPTERS 12, 14, 15 AND 16. CH 12 BREAK EVEN ANALYSIS Sales price EBIT = 0 = units X per unit Break-even level of units variable cost - units X sold per unit total fixed + sold cost total fixed cost = sales price variable cost per unit − per unit Total fixed cost S* = F/ [1 – (VC / S)] Break-even level of revenues = variable cost 1revenues Degree of operating leverage DOLs = Q (P – V) / [Q (P – V) – F] DOLs = revenue before fixed cost / EBIT = S – VC / [S - VC – F] Degree of financial leverage DFLEBIT = EBIT / (EBIT - I) Degree of combined leverage DCLS = (DOLS) X (DFLEBIT) EBIT – EPS indifference point: EPS: Stock plan (EBIT – I)(1 – t) – P / Ss = EPS: Bond plan (EBIT – I)(1 – t) – P / Sb EBIT = [Ib – Is (Sb / Ss ] / [ 1 - (Sb / Ss) ] 14 SHORT-TERM FINANCIAL PLANNING CURRENT ASSETS AS A PERCENTAGE OF SALES = Current assets / sales Projected current assets = projected sales X (current assets / sales) Projected addition net income cash dividends X 1 − = projected sales X to retained earnings sales net income Discretionary financing = projected total assets – projected liabilities – projected owner’s equity needed (DFN) predicted change predicted change − DFN = in total assets predicted change − in spontaneous liabilities in retained earnings External financing needs (EFN) EFN = predicted change in total assets – change in retained earnings ...

Words: 532 - Pages: 3

...per (it can fluctuate) Imputed costs Equity capital also has cost (investors expect a return) but these do not appear on the income statement⇒because there is no paper that says how much the company has to pay this cost must be estimated (imputed) The economist would subtract these cost but an accountant does not, the difference is important because many decisions are based on the numbers in the income statement: wages, bonuses, etc 4 Chapter 2. Evaluating financial performance Return on equity Most popular tool to evaluate financial performance ⇒ it is a measurement of efficiency⇒ earnings investedDOLLAR NETincome Is the basic formula but can be redefined as shareholdersEQUITY ROE = NETincome Sales Assets ⋅ ⋅ Sales Assets shareholdersEquity ROE = ROE = PROFITm argin ⋅ ASSETturnover ⋅ FINANCIALleverage Profit margin: earnings out of every dollar of sale NETincome ‐ Return on assets ROA = Assets GROSSprofits ‐ Gross margin: ⇒ used to find out the fixed and the variable costs Sales Asset turnover: sales generated for every dollar of assets employed Low: assets intensive industry cos tgoodssold Inventory ending Inventory turnover: Collection period: shows the company’s management of accounts receivable ⇒ average time ......

Words: 4665 - Pages: 19

...STAT 346/446 - A computer is needed on which the R software environment can be installed (recent Mac, Windows, or Linux computers are sufficient).We will use the R for illustrating concepts. And students will need to use R to complete some of their projects. It can be downloaded at http://cran.r-project.org. Please come and see me when questions arise. Attendance is mandatory. Topics covered in STAT 346/446, EPBI 482 Chapter 5 – Properties of a Random Sample Order Statistics Distributions of some sample statistics Definitions of chi-square, t and F distributions Large sample methods Convergence in probability Convergence in law Continuity Theorem for mgfs Major Theorems WLLN CLT Continuity Theorem Corollaries Delta Method Chapter 7 – Point Estimation Method of Moments Maximum Likelihood Estimation Transformation Property of MLE Comparing statistical procedures Risk function Inadmissibility and admissibility Mean squared error Properties of Estimators Unbiasedness Consistency Mean-squared error consistency Sufficiency (CH 6) Definition Factorization Theorem Minimal SS Finding a SS in exponential families Search for the MVUE Rao-Blackwell Theorem Completeness Lehmann-Scheffe Location and scale invariance Location and scale parameters Cramer-Rao lower bound Chapter 9 - Interval Estimation Pivotal Method for finding a confidence interval Method for finding the “best” confidence interval Large sample confidence......

Words: 321 - Pages: 2