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Hypothesis Test Conclusion The important caution brings up the following idea: If , then, ( ) ( ) ( ) ( ) Similarly, If , then, ( ) ( ) ( ) ( ) The probability that we reject the null hypothesis when it is false is referred to as the power of the test. We summarize these in the table below: Don‟t Reject Truth True False Reject Example 4: The college dropout rate for a particular county is known to be 30%. The educational board of a city within the county believe its dropout rate is significantly lower. The board follows 60 students and, of them, 15 dropout. The board wants to run a statistical hypothesis test with to determine whether their belief is true. Describe the hypothesis test by: a. Writing competing hypotheses b. A decision rule for rejecting c. A decision criterion rule Statistics for Decision-Making in Business © Milos Podmanik Page 216
SOLUTION: d. A generic conclusion statement a.) Under the null hypothesis, . We want to test to see if . Thus: b.) We will reject if the probability of observing something as or more extreme as 15 out of 60 dropouts ( ) under the assumption of the null hypothesis is less than or equal to 0.05. That is: ( ) c.) We will reject if the observed value of is smaller than some cutoff value of . That is, it might be the case that would have to be smaller than, say, 13 in order for us to reject the null hypothesis. d.) Based on sample evidence, we (choose from below) a. Reject in favor of b. Fail to reject . We do not accept as true, but we don‟t have evidence to conclude otherwise. As we see from the above example, our hypothesis test needs to have a structured layout. We need to know ahead of time what we‟ll do. It is tempting, but we cannot determine our rejection criterion based on what the sample data tells us! In practice, you can carry this type of philosophy, but you increase the error rate. Consider, for example, the scenario wherein you take an exam for a biology class. You get the results back and look at what you missed. You say, “oh, of course I should have put that! I knew that!” If you told that to the instructor, she may say, “sorry, you didn‟t demonstrate that on the exam.” Without surprise, we expect this response. Why Because, it is the test that helps to determine our level of understanding! It is not the other way around. If the instructor allowed you to change your answer, then the test wouldn‟t really be demonstrating what you knew at that time of the test. A hypothesis test is quite analogous. We carry one out because we have a hunch. Always think back to this statement: If you dig long enough in your data, you will find something! This, however, looks upon the digging process as a negative thing since it does not justify the decision questions. In fact, it creates a high likelihood that we are observing a coincidence and not a solid finding at all! Thus, we increase the probability of error exponentially! Statistics for Decision-Making in Business © Milos Podmanik Page 217
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Statistics for Decision- Making in
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A Note to Students This book is far
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6.3 Confidence Interval for ̂ 202
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which has undergone no additional r
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of transporting such an animal. Not
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Although far from last, we will con
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Account Type Revenue ($) New $5,296
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For the present time, we‟ll proce
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Row Labels Average of Revenue ($) M
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(SOURCE: Essentials of Modern Busin
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1.3Statistics in Excel When conduct
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We can make it a bit more presentab
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For measures such as the range, Exc
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We get: We can do the same for Old.
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Chapter 2 Visual Representations of
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We now have to enter a formula for
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We know from the data that this val
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Alternatively, it is possible to in
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16 14 12 10 8 6 Series1 4 2 0 SD D
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Selected the copied graph. In Chart
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2. The following data represents pe
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Frequency 2.2 Visualizing Quantitat
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You can either choose to have Excel
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If we had additional variables, the
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Select PivotChart and select the fi
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Frequency To make solid black lines
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Relative Frequency Histogram of Cal
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a. Using Excel, find the mean and r
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̅ ̅ account for this discrepancy,
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2.3.2 Percentile Another useful too
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2.3.4 Rank What if, on the other ha
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Check the “Analysis ToolPak” an
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You‟ll immediately notice that a
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̅ 2.4 Descriptive Statistics - Var
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√ ∑( ̅) √ √ This is what w
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This gives us a nice measure of how
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We can immediately see the mean and
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Lastly, a normal curve is the most
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Frequency ̅ Histogram of TV's Owne
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58.3 34.6 35.5 45.4 38.6 63.8 53.9
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Chapter 3 Probability and Decision
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Proportion of Rainy Days 1.2 Propor
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This means that if samples of HFCS
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Are we confident in accusing a lung
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3.2 Joint Probability In the previo
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Company 1 Choices Y Y Y Y Y Y Y Y N
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There is less than a 1% chance that
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We can now see that the situation i
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sequence, one ball is chose at rand
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3.3 Probability of Unions Imagine t
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Physical .20 .10 .30 Mental .40 .30
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SOLUTION: We first arrange this inf
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Homework Problems - 3.3 1. A gaming
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3.4 Conditional Probability In many
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) : The Arizona Cardinals make it t
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We could just as well have written,
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That is, the probability that the c
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This can happen in one of two ways:
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f. All numerical red cards are remo
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3.5 Combinations and Permutations R
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1 st Bushel 2 nd Bushel U1 U1 U2 U2
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Combination - Order Does NOT Matter
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( ) There is only a .9% chance that
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1. Are repeats/replacements allowed
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Object 4 Object 5 Also notice that
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e. 2. Your classmate was absent whe
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3.6 Expected Value Imagine that you
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An expected value is actually not s
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Where The expected value is: , - (
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SOLUTION: We first note that there
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Probability 0.7 0.6 0.5 0.4 0.3 0.2
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Statistics for Decision-Making in B
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For men, we see that the most frequ
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So, we had 10 trials and wanted to
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Example 1: A fair-two sided coin is
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( ) ( ) ( ) ( ) Example 2: A fair,
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Expected Value of a Binomial Random
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f. What is the probability that g.
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Probability For instance, we see th
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Density 0.25 Time Speng Waiting in
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Suppose we wish to find ( ), that i
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Page 169 and 170: a. What is the probability that the
Page 171 and 172: Statistics for Decision-Making in B
Page 173 and 174: As you can see, this is a difficult
Page 175 and 176: We wish to know the area of the sha
Page 177 and 178: We can easily find that the probabi
Page 179 and 180: In the second section, we can check
Page 181 and 182: Chapter 6 Sampling Distributions an
Page 183 and 184: Thus, the standard deviation would
Page 185 and 186: 1.7 to 1.8 1.8 to 1.9 1.9 to 2 2 to
Page 187 and 188: 2.4 to 2.5 2.5 to 2.6 2.6 to 2.7 2.
Page 189 and 190: The Central Limit Theorem (CLT) has
Page 191 and 192: can set this up in our applet by ha
Page 193 and 194: 3) We cannot use this sample to cal
Page 195 and 196: Thus, we need to find the lower and
Page 197 and 198: We see that the standard deviation
Page 199 and 200: √ The lower limit is: √ And the
Page 201 and 202: following data was collected on the
Page 203 and 204: { So, we have a set of twenty 1‟s
Page 205 and 206: DULY CAUTIONED: The assumptions her
Page 207 and 208: Homework Problems -6.2 1. In a samp
Page 209 and 210: Since this is a mathematical questi
Page 211 and 212: Example 3: Many older homes have el
Page 213 and 214: Hypothesis Test Conclusion Test Say
Page 215: Hypothesis Test Conclusion Since yo
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Page 221 and 222: 1.2 Descriptive VS. Inferential Sta
Page 223 and 224: Relative Frequency 35% 30% 25% 20%
Page 225 and 226: Symmetric: 35 30 25 20 15 10 5 0 10
Page 227 and 228: Repair Cost Mean 971 Standard Error
Page 229 and 230: el freq Nitrous Oxide (thous. Tons)
Page 231 and 232: Probability Pizza Size Distribution
Page 233 and 234: e. ( ) f. ; the average response ti
Page 235 and 236: 2. a. The long-run proportion of al
Page 237 and 238: e. 20% of all children are expected
Page 239 and 240: e. The middle 50% score between abo
Page 241 and 242: 5. b. This might indicate that the
Page 243 and 244: ̅ We have that ̅ and √ . So our
Page 245 and 246: 6. This is the probability that we
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