6. It is believed that the mean right hand grip strength of men between 20 and 40 years of age in the USA is 86.3 lbs. It is now of interest to perform a hypothesis test concerning the mean grip strength of men between 20 and 40 years of age in the country of Techavia. (a) If we are looking for evidence that the mean grip strength in Techavia is different from 86.3 lbs., state the null and alternative hypotheses for the hypothesis test. H0:  = 86.3 (The mean grip strength is 86.3 lbs.) H1:   86.3 (The mean grip strength is different from 86.3 lbs.) (b) Is the hypothesis test one-sided or two-sided? Since we are looking for evidence that the population mean is different from the hypothesized value 86.3 in either direction, then the test is two-sided (c) Describe what it would mean to make a Type I error in this hypothesis test and what it would mean to make a Type II error in this hypothesis test. Making a Type I error means the mean grip strength is actually 86.3 lbs., but we mistakenly conclude that it is different from 86.3 lbs. Making a Type II error means the mean grip strength is actually different from 86.3 lbs., but we mistakenly conclude that it is equal to 86.3 lbs.
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VO or VC ● VO ● Lower max grip ● Relaxed full grip ● Grip force determined by # of rubber bands ● VC ● High max grip ● Sustained tension for grip ● Graded prehension with excellent “feel”
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Correlations Pearson Correlation Sig. (1-tailed) N grip age grip age grip age grip 1.000 .770 . .002 12 12 age .770 1.000 .002 . 12 12 Model Summaryb Model 1 R .770a R Square .593 a. Predictors: (Constant), age b. Dependent Variable: grip Adjusted R Square .552 Std. Error of the Estimate 8.390
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6. It is believed that the mean right hand grip strength of men between 20 and 40 years of age in the USA is 86.3 lbs. It is now of interest to perform a hypothesis test concerning the mean grip strength of men between 20 and 40 years of age in the country of Techavia. (a) If we are looking for evidence that the mean grip strength in Techavia is different from 86.3 lbs., state the null and alternative hypotheses for the hypothesis test. H0:  = 86.3 (The mean grip strength is 86.3 lbs.) H1:   86.3 (The mean grip strength is different from 86.3 lbs.) (b) Is the hypothesis test one-sided or two-sided? Now look at the definitions for one-sided and two-sided tests. (c) Describe what it would mean to make a Type I error in this hypothesis test and what it would mean to make a Type II error in this hypothesis test.
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6. It is believed that the mean right hand grip strength of men between 20 and 40 years of age in the USA is 86.3 lbs. It is now of interest to perform a hypothesis test concerning the mean grip strength of men between 20 and 40 years of age in the country of Techavia. (a) If we are looking for evidence that the mean grip strength in Techavia is different from 86.3 lbs., state the null and alternative hypotheses for the hypothesis test. H0:  = 86.3 (The mean grip strength is 86.3 lbs.) H1:   86.3 (The mean grip strength is different from 86.3 lbs.) (b) Is the hypothesis test one-sided or two-sided? Since we are looking for evidence that the population mean is different from the hypothesized value 86.3 in either direction, then the test is two-sided (c) Describe what it would mean to make a Type I error in this hypothesis test and what it would mean to make a Type II error in this hypothesis test. Now look at the definitions for Type I and Type II error.
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Autonomous saw  Inputs: Pressure sensors, touch sensors, position sensors, position sensor, safety sensors, Variable Frequency Drive (VFD) speed feedback (4-20 mA) for saw blade  Outputs: Open/Close clamps command, Raise/Lower saw, Move pipe Forward/Backward, coolant pump On/Off, saw blade speed command (4-20 mA)  Controller: Allen Bradley Programmable Logic Controller (PLC)  Application: Cut long pipes into specified length. Tolerance is +/- 0.005 in.  Sequence: 1. Select length and press Start 2. Close rear clamps 3. Close front clamps 4. Lower saw and cut 5. Retract pipe 6. Raise saw and open front clamps 7. Index pipe 8. Close from clamps 9. Repeat the sequence until end of pipe http://www.google.com/imgres? q=pipe+cutting+saw+machine&um=1&hl=en&biw=1024&bih=562&tbm=isch&t bnid=WliuZzoKoLVvgM:&imgrefurl=http://www.ecvv.com/product/1872085.ht ml&docid=OFdWWiTxazEgzM&imgurl=http://upload.ecvv.com/upload/Product/ 20093/China_Highly_efficient_Pipe_Cutting_Band_Saw_Machine20093111546 170.jpg&w=1024&h=768&ei=-NTgT4KvK8ry0gHP0oG3Dg&zoom=1 8
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Solution Methodology 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 A Razor 1 Attributes B C D E F G H J K L Number buying ours =SUM(M6:Q6) 2 Level 2 2 2 2 Part-worths for segments 1 2 3 =VLOOKUP($B5,$K$16:$Q$19,E$21+2) 4 5 Wide 1 1 4 3 4 Textured Plastic 1 4 4 1 3 Maximums Black 1 4 4 3 1 Buy ours? Lube 2 2 2 =SUM(E4:E7) 2 3 5 11 14 9 11 Level 3 3 3 3 Part-worths for segments 1 2 3 4 5 Long 2 4 3 3 3 Smooth Plastic 4 1 3 1 1 Red 3 2 2 4 4 None 2 2 2 2 4 11 9 10 10 12 Attribute Level 1 1 1 1 Part-worths for segments 1 2 3 4 5 Slim 1 1 1 4 4 Rubber 4 1 3 2 4 Blue 4 1 3 3 1 Aloe 4 4 3 4 4 13 7 10 13 13 Attribute Handle Grip Color Strip Totals Razor 2 Attributes Attribute Handle Grip Color Strip Totals Razor 0 Attributes Handle Grip Color Strip Totals I M 13 0 Attribute Level Handle 1 Slim 2 Wide 3 Long N O P =MAX(D26,D8,D17) 11F(D26
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Optimal Solution 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 A Razor 1 Attributes B C D E F G H Level 1 1 3 1 Part-worths for segments 1 2 3 4 5 Slim 1 1 1 4 4 Rubber 4 1 3 2 4 Red 3 2 2 4 4 Aloe 4 4 3 4 4 12 8 9 14 16 Level 3 1 1 1 Part-worths for segments 1 2 3 4 5 Long 2 4 3 3 3 Rubber 4 1 3 2 4 Blue 4 1 3 3 1 Aloe 4 4 3 4 4 14 10 12 12 12 Attribute Level 1 1 1 1 Part-worths for segments 1 2 3 4 5 Slim 1 1 1 4 4 Rubber 4 1 3 2 4 Blue 4 1 3 3 1 Aloe 4 4 3 4 4 13 7 10 13 13 Attribute Handle Grip Color Strip Totals Razor 2 Attributes Attribute Handle Grip Color Strip Totals Razor 0 Attributes Handle Grip Color Strip Totals I J K L Number buying ours 5 M N O P Q Maximums Buy ours? 14 1 10 1 12 1 14 1 16 1 Attribute Level Handle 1 Slim 2 Wide 3 Long Sgmt 1 Sgmt 2 Sgmt 3 Sgmt 4 Sgmt 5 1 1 1 4 4 1 1 4 3 4 2 4 3 3 3 Grip 1 Rubber 2 Textured Plastic 3 Smooth Plastic 4 1 4 1 4 1 3 4 3 2 1 1 4 3 1 1 Blue 2 Black 3 Red 4 1 3 1 4 2 3 4 2 3 3 4 1 1 4 1 Aloe 2 Lube 3 None 4 2 2 4 2 2 3 2 2 4 2 2 4 3 4 Color Decision Models -- Prof. Juran Strip 41
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46.40 and 85.60 We are 95% confident that the grip strength for a randomly selected 20-year old right-handed male will be between 46.40 and 85.60 lbs. OR At least 95% of 20-year old right-handed males have a grip strength between 46.40 and 85.60 lbs. (n) For what age group of right-handed males will the confidence interval for mean grip strength and the prediction interval for a particular grip strength both have the smallest length? 18 year olds
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Store Raw Data  Useful Information Excel • Proper Data Set in Excel • Field names in first row • Field names say what sort of data can go in the column • Records in subsequent rows • Record = row = collection of bits of raw data = set of related data • Data Analysis in Excel: • Create useful information from raw data to help make decisions • We used: • Formulas like SUMIFS • Sort, Filter, PivotTables, Excel Tables, Relationships & Data Model & Power Query Access • Proper Table (Data Set) in Access • Field names in first row • Add Data Type and Field Properties so that bad raw data does not enter the table • Note: In Excel, we saw an example of "Data Type" in Power Query • Each record must have unique identifier (Primary Key) • In order to prevent duplicate records • Examples: Student ID, Invoice Number, Product ID • In Excel, we saw an example of "Primary Key" when we create Relationships between tables for our PivotTable reports. • Records in subsequent rows • Record = row = collection of bits of raw data = set of related data • Data Analysis in Access: • We will use: Queries and Reports • In Excel, we saw an example of "Queries" in Power Query • We will create relationships between tables so that we can create useful information from more than one table at a time. • In Excel, we saw an example of “Relationships" when we create Relationships between tables for our PivotTable reports. 4
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Saws To the left is a coping saw. A coping saw is used to cut intricate, small patterns in wood working, especially interior cuts. The blades are flexible for turning, but are still thin and small and can break easily. Coping saw blades can be removed easily to slide the blade into tight spaces. A keyhole saw is picture to the right. This type of saw is used especially in sheetrock work, as it can pierce straight through and cutout the shapes you need.
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Research Question: Effects of Continued Exposure to Pornography • Ss were 80 M and 80 F students at IU • 3 experiments groups saw X-rated (but nonviolent films over 6 weeks – G1 saw 6 erotic films (4 hours: 48 minutes) – G2 saw 3 erotic films (2:24) and 3 neutral films – G3 saw 6 neutral films (4:48) – G4 saw no films
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