Last updated: August 18, 2019
Topic: Technology
Sample donated:

By and large, there are two groups of salmon. One is the Atlantic salmon. It lives in the North Atlantic Ocean between North America and Europe. The others are species of Pacific salmon that live in the North Pacific Ocean. Like the Atlantic salmon, they live in the ocean and besides in the rivers of western North America and eastern Asia. The salmon is an anadromous ( uh-NAD-droh-muhss ) fish. This means that it spawns in fresh water but spends much of its life at sea.

When an Atlantic salmon reaches the age of two, it leaves its place in the North Atlantic. It begins a migration to the same topographic point in the river or watercourse where it was born. It spawns and so returns to the ocean for two old ages. After constructing up its strength, it leaves the ocean and returns to the rivers to engender yet once more.The growing of pink-orange fish graduated table has been studied and their growing were measured by breadth for first twelvemonth when they were remaining in ocean environment. The pink-orange fish graduated tables have been enlarged 100 times, so that the ratings are made in hundredths of an inch. In this assignment, we were given a undertaking that consists of a set of measuring which was gathered by the Alaska Department of Fish and Game as given in Table A. ( Courtesy of K.

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Jensen and B. Van Alen. ) The content of assignment is to carry on an analysis on a spread diagram, a fitted line and a finding if I?1 differs from nothing. Furthermore, we were asked to happen a 95 % of assurance interval for the population mean when the fresh water growing is 100.Three types of arrested development analyses have been completed in this assignment, viz. all pink-orange fish arrested development analysis, male pink-orange fish arrested development analysis, and last but non least, female salmon fish arrested development analysis. All the analysis shows the arrested development of the marine growing over the freshwater growing.

Empirical Models

Today, many jobs in technology and the scientific discipline involve a survey or analysis of the relationship between or more variables. There are two types of empirical theoretical accounts that are deterministic and non deterministic. Deterministic theoretical accounts are those are able to foretell the supplanting absolutely, such as the force per unit area of a gas container is related to the temperature and yet most of the state of affairss are deterministic in existent word analysis. Therefore, there is a nondeterministic mode called arrested development theoretical account is used to pattern and research the relationships between these variables. However, there are some premises need to be made in our analysis for the empirical theoretical accounts that we are traveling to utilize. We assume that there is merely independent or forecaster variable ten and the relationship with the response Y are additive. The information aggregation of the informations are represented utilizing a spread diagram which is a graph on which each ( xi, yi ) brace is represented as a point plotted in a planar co-ordinate system. Base on the spread diagram, it is likely sensible to presume that the mean of the random variable Y is related to x by the undermentioned straight-line relationship:where the incline and intercept of the line are called arrested development coefficients.

Since the mean of Y is non precisely a additive map of x, therefore it ‘s more appropriate to show it as in the undermentioned equation:, where is the random error term.This theoretical account is besides known as the simple additive arrested development theoretical account, because it has merely one independent variable or regressor. Since there is no theoretical cognition of the relationship between ten and Y, and the pick of the theoretical account is based on review of a spread diagram, therefore it can be said that the arrested development theoretical account as an empirical theoretical account.

Simple Linear Regression

The instance of simple additive arrested development considers a individual regressor variable or forecaster variable ten and a dependant or response variable Y. Thus, Y can be described by the theoretical accountwhere is a random mistake with average mistake and ( unknown ) discrepancy. The random mistakes matching to different observations and are besides assumed to be uncorrelated random variables. Suppose that we have n braces of observations, an estimated arrested development line with the “ best tantrum ” is needed to be drawn in the spread diagram. The German scientist Karl Gauss ( 1777 – 1855 ) proposed gauging the parametric quantities and in Equation 1-1 to minimise the amount of the squares of the perpendicular divergence. We call this standard for gauging the arrested development coefficients the method of least squares. Using Equation 1-2, we may show the n looks in the sample as( 1-3 )and the amount of the squares of the divergences of the observations from the true arrested development line is( 1-4 )The least squares estimations of the intercept and incline in the simple additive arrested development theoretical account are( 1-5 )( 1-6 )where and.The fitted or estimated arrested development line is hence( 1-7 )Note that each brace of observations satisfies the relationshipwhere is called the remainder.

The residuary describes the mistake in the tantrum of the theoretical account to the ith observation. The remainders are used to obtain the information about the adequateness of the fitted theoretical account.Notational, it is on occasion convenient to give particular symbols to the numerator and denominator of Equation 1-6. Given informations, ,aˆ¦ , , allow( 1-8 )and( 1-9 )Another unknown parametric quantity in the arrested development theoretical account, ( the discrepancy of the error term, ) .

The remainders are used to obtain an estimation of. The amount of squares of the remainders, or known as the mistake amount of squares, is( 1-10 )It ‘s so showed that the expected value of the mistake amount of squares is. Therefore an indifferent calculator of is( 1-11 )By replacing into Equation 1-10, and simplifying,( 1-12 )where is the entire amount of squares of the response variable Y.

Hypothesis Trials in Simple Linear Regression

3.3.1 Use of t-Tests

Suppose we wish to prove the hypothesis that the incline equals a changeless, . The appropriate hypotheses areHolmium:H1: ( 1-13 )where we assumed a reversible option. Since the mistakes are, it follows straight that the observations Yi are.

Now is a additive combination of independent normal random variables, and accordingly, is, utilizing the prejudice and discrepancy belongingss of the incline discussed earlier. Besides, has a chi-square distribution with n-2 grade of freedom, and is independent of. As a consequence of those belongingss, the statistic( 1-14 )follows the t-distribution with n -2 grade of freedom under Ho: . We would reject Ho: if( 1-15 )A similar process can be used to prove hypotheses about the intercept.

To proveHolmium:H1: ( 1-16 )We would utilize the statistic( 1-17 )and reject the void analysis if computed value of this trial statistic, , is such that.A really of import particular instance of hypotheses of Equation 1-13 isHolmium:H1: ( 1-18 )These hypotheses relate to the significance of arrested development. Failure to reject Ho: is tantamount to reasoning that there is no additive relationship between ten and Y.

3.3.2 Analysis of Variance Approach to Test Significance of Regression

To prove significance of arrested development, the analysis of discrepancy can be used.

The process partitions the entire variableness in the response variable into meaningful constituents as the footing for the trial. The analysis of discrepancy individuality is as follows:( 1-19 )The two constituents on the right-hand-side of Equation 1-19 step, severally, the sum of variableness in accounted for by the arrested development line and the residuary fluctuation left unexplained by the arrested development line. Using the Equation 1-10 ( the mistake amount of squares ) and the the arrested development amount of squares, we can calculate the undermentioned equation:( 1-20 )where is the entire corrected amount of squares of Y.Since, and that and are independent chi-square random variables with n-2 and 1 grades of freedom, severally.

Therefore, if the void hypothesis Ho: is true, the statistic,( 1-21 )follows the distribution, and we would reject Ho if. The measures and are called average squares. This trial process is normally arranged in an analysis of discrepancy tabular array.

Assurance Time intervals

3.4.1 Assurance Time intervals on Slope and Intercept

Under the premise that the observations are usually and independently distributed, a assurance interval on the incline in simple additive arrested development is( 1-22 )Similarly, a assurance interval on the intercept is( 1-23 )

3.4.2 Assurance Time intervals on the Mean Response

A assurance interval about the average response at the value of, say, is given by( 1-24 )where is computed from the fitted arrested development theoretical account.

Adequacy of the Regression Model

Suiting a arrested development theoretical account requires several premises. Appraisal of the theoretical account parametric quantities requires the premise that the mistakes are uncorrected random variables with average nothing and changeless discrepancy. Trials of hypotheses and interval appraisal require that the mistakes be usually distributed. In add-on, we assume that the order of the theoretical account is right ; that is, if we fit a simple additive arrested development theoretical account, we are presuming that the phenomenon really behaves in a additive or first-order mode.

3.5.1 Residual Analysis

The remainders from a arrested development theoretical account are, where is existent observation and is the matching fitted value from the arrested development theoretical account. Analysis of the remainders is often helpful in look intoing the premise that the mistakes are about usually distributed with changeless discrepancy, and in finding whether extra footings in the theoretical account would be utile.As an approximative cheque of normalcy, the experimenter can build a frequence histogram of the remainders or a normal chance secret plan of remainders. We may besides standardise the remainders by calculating.

If the mistakes are usually distributed, approximated 95 % of the standardised remainders should fall in the interval ( -2, +2 ) . Remainders that are far outside this interval may bespeak the presence of an outlier, that is, an observation that is non typical of the remainder of the informations. Assorted regulations have been propose for flinging outlier, but outliers sometimes provide of import information about unusual fortunes of involvement to experimenters and hence should non be automatically discarded.

3.5.2 Coefficient of Determination ( R2 )

The coefficient of finding is( 1-25 )The coefficient is frequently used to judge the adequateness of a arrested development theoretical account.

However the statistic R2 should be usage with cautiousness, because it is ever possible to do R2 integrity by merely adding adequate footings to the theoretical account. For illustration, we can obtain a “ perfect ” tantrum to n informations points with a multinomial of grade n – 1. In add-on, R2 will ever increase if we add a variable to the theoretical account, but this does non needfully connote that the new theoretical account is superior to the old 1. Unless the mistake amount of squares in the new theoretical account is reduced by an sum equal to the original mistake mean square, the new theoretical account will hold a larger mistake mean square than the old one, because of the loss of one mistake grade of freedom. Therefore, the new theoretical account will really be worse than the old one.

4.

0 METHODS

In this subdivision, we discuss about the methods used to execute arrested development analysis. The methods used to calculate all the consequences are shown. Microsoft Office Excel 2007 is used as our chief analyses package to carry on the analysis. Our analyses include obtaining spread diagram, linear fitted line, trial hypotheses and the assurance interval. All these analyses can be done by utilizing one of the maps of Microsoft Excel that is ‘Regression ‘ in ‘Data Analysis ‘ .

4.1 Stairss of Using ‘Data Analysis ‘ in Microsoft Excel

First of wholly, there is an of import measure to make before we can get down to analyse our informations that is make certain the handiness of map ‘Data Analysis ‘ in our Microsoft Excel. ‘Data ‘ from the bill of fare check is chosen to look into the handiness of the map. If ‘Analysis ‘ column is non found, so add in map is needed to obtain the map of informations analysis. Function ‘Data Analysis ‘ can be retrieved by custom-making the speedy entree toolbar utilizing the check of ‘Add-Ins ‘ .

After finish the ‘Add- Ins ‘ procedure, so merely we can continue to analyse our informations.Then, ‘Data Analysis ‘ is clicked and an direction box ( Data Analysis ) is popped out. Following, ‘Regression ‘ is chosen and another direction box ( Regression ) . We started our input procedure by first input the ‘Input Y Range ‘ , which the information of dependent / response variables is keyed in ; and 2nd is ‘Input X Range ‘ , which the information of independent / regressor variables is keyed in. The ‘Input Y Range ‘ is the information of First Year Marine Growth, while the ‘Input X Range ‘ is the information of Freshwater Growth.

For our assignment, merely ‘Label ‘ and ‘Line Fit Plot ‘ is ticked and any of the end product options can be selected based on the users. Last, the drumhead end product is shown after the ‘OK ‘ button is pressed.

2.4 Trial Hypothesiss

In order to find whether the fitted line follows additive relationship, trial hypotheses is done by comparing the value of F in ANOVA with fI±,1, n-2. The value of F in ANOVA can be obtained straight from the arrested development drumhead end product. The appropriate hypotheses are H0: I?1=0 ( there is no additive relationship between X and Y ) and H1: I?1a‰ 0 ( there is additive relationship between X and Y ) . Therefore, H0 is rejected if F & gt ; fI±,1, n-2 and Ho is failed to reject if F & lt ; fI±,1, n-2. When H0 is rejected, it means that the dependant and independent variable has linear relationship.

2.5 Assurance Time interval

In order to happen the 95 % assurance interval for the population mean when the when the fresh water growing is 100, the expression of assurance interval about the average response is used. The unknown values are obtained from arrested development sum-up end product and applying of equation as shown below in order to obtain the consequences.

5.0 Consequences

5.

1 Consequences for All Fishs:

Freshwater GrowthMarine Growth14713144440513911344642216013743842899121437469120139405424151144435402115161394440121107406410109129440366119123414422130148444410110129465352127119457414100134498396115139452473117140418398112126502434116116478395981125003349811758945583974804398513442451188884554329899439381741054234185811241147511498484436888044743177139448515869745050886103493429659349542012785470424916045445676115430491441134484744291512421501094174515712246644242Table 5.1: Datas for All Fishs68496363SUMMARY End product:Arrested development StatisticssMultiple R0.172822174R Square0.029867504Adjusted R Square0.017429908Standard Error40.7184312Observations80Analysis of variance

A

dfUnited states secret serviceMultiple sclerosisFSignificance FArrested development13981.4801253981.

4801252.4013887840.125275736Residual78129323.

26991657.990639Entire79133304.75

A

A

A

A

CoefficientsStandard ErrorT StatP-valueIntercept469.616057118.3150765325.640955219.42156E-40FreshM-0.2579200860.

166438551-1.5496415020.125275736Lower 95 %Upper 95 %Lower 95.0 %Upper 95.0 %433.

1535413506.078573433.1535413506.078573-0.5892737810.07343361-0.589273780.07343361Figure 5.

1: Scatter Diagram & A ; Fitted Line of All FishRESIDUAL OUTPUT:ObservationPredicted Marine GrowthRemainders1431.701812.29822433.765212.234833428.34889.6511574444.

082-7.081975438.6656-33.

66566430.67014.3298767439.9552-45.

95528438.4077-32.40779441.5028-1.

5027710438.9236-24.923611436.08647.

91355412441.244823.7551513436.

860220.1397914443.82454.

1759515439.955212.0447516439.4394-21.439417440.

72961.2709918439.697338.3026719444.339955.6601120444.3399144.

660121448.208731.7913122447.6928-23.692823446.91918.0809124444.

3399-5.3398925450.53-27.5326454.6567-43.656727440.

213243.7868328446.91910.0809129449.7562-1.7562130447.

43492.5650731447.434945.5650732452.

851342.1487533436.860233.1397934446.14537.85467135450.0141-20.014136458.

2676-10.267637458.783453.2165938456.

7201-39.720139454.914611.0853940458.783437.2165941435.8285-30.

828542440.4711-18.471143434.281-6.2810144438.407730.

5922745433.7652-9.7651746432.4756-30.475647428.090911.9090848442.0186-32.

018649436.3444-70.344450437.8919-15.891951431.4439-21.443952436.3444-84.

344453438.9236-24.923654435.0548-39.054855433.765239.2348356433.5072-35.

507257437.1181-3.1181358439.6973-44.

697359440.729-106.72960439.439415.

5605961444.5978-5.5978162435.

054875.9452363446.9191-14.919164444.082-63.08265442.

5344-24.534466440.72934.2709967444.3399-8.3398968448.9825-17.

982569433.765281.2348370444.597863.4021971443.0503-14.

050372445.6295-25.629573447.6928-23.

692874454.14091.85914875439.955251.0447576440.

471133.5289177446.1453-25.145378441.

50289.49723279438.14983.

85019380452.0775-89.0775

5.2 Consequences for Males ‘ Fish:

Freshwater GrowthMarine Growth147834444801398544642416088438455999843743912074405423151584354111151143944841218840644710977440448119864144501308644449311065465495127127457470100914984541157645243011744418448112425025121165047841798575004669842589496Table 5.2: Datas for Males FishSUMMARY End product:Arrested development StatisticssMultiple R0.190576376R Square0.

036319355Adjusted R Square0.010959338Standard Error37.05170732Observations40Analysis of variance

A

dfUnited states secret serviceMultiple sclerosisFSignificance FArrested development11966.

0974311966.0974311.432150260.238826934Residual3852167.

502571372.829015Entire3954133.6

A

A

A

A

CoefficientsStandard ErrorT StatP-valueIntercept478.353924320.2952291823.

5697722.77485E-24Freshwater Growth-0.2364405120.197573001-1.1967248060.238826934Lower 95 %Upper 95 %Lower 95.0 %Upper 95.

0 %437.2683812519.4394675437.2683812519.4394675-0.6364061390.163525116-0.6364061390.

163525116Figure 5.2: Scatter Diagram & A ; Fitted Line of Males FishRESIDUAL OUTPUT:ObservationPredicted Marine GrowthRemainders1443.59716910.

4028308942445.48869320.5113068013440.5234425-2.5234424544454.9463137-17.

946313675449.9810629-44.981062926442.6514071-7.

6514070597451.1632655-57.163265488449.

7446224-43.744622419452.5819086-12.5819085510450.2175034-36.

2175034311447.6166578-3.61665780512452.34546812.

6545319613448.32597938.6740206614454.709873243.2901268415451.16326550.8367345216450.6903845-32.

6903844617451.87258750.1274129818450.92682527.0731750319455.182754244.8172458220455.

1827542133.817245821458.729361921.2706381522458.2564808-34.2564808323457.

5471593-2.54715929624455.1827542-16.

1827541825460.8573265-37.8573264626464.

6403746-53.6403746527451.39970632.6002940128457.5471593-10.547159329460.

1480049-12.1480049230458.0200403-8.02004031931458.020040334.9799596832462.985291132.

0147089333448.325979321.6740206634456.8378378-2.83783776135460.

3844454-30.3844454436467.9505418-19.9505418137468.423422843.5765771738466.5318987-49.5318987439464.

87681521.12318484140468.423422827.57657717

5.3 Consequences for Females ‘ Fish:

Freshwater growingMarine growing1319740543911313442251113788428432121994693811391054244181441124024751619844043610780410431129139366515123974225081481034104291299335242011985414424134603964561391154734911401133984741269143442111610939545111212233444211768455363Table 5.3: Datas for Females FishSUMMARY OUTPUTArrested development StatisticssMultiple R0.040178762R Square0.001614333Adjusted R Square-0.

024658974Standard Error41.54801703Observations40Analysis of variance

A

dfUnited states secret serviceMultiple sclerosisFSignificance FArrested development1106.0666754106.06667540.0614438410.805562865Residual3865597.

033321726.237719Entire3965703.1

A

A

A

A

CoefficientsStandard ErrorT StatP-valueIntercept420.627480835.0037883512.

016627361.63513E-14Freshwater Growth0.0742218080.2994279620.2478786810.805562865Lower 95 %Upper 95 %Lower 95.

0 %Upper 95.0 %349.7660166491.4889451349.7660166491.

4889451-0.5319384060.680382023-0.

5319384060.680382023Figure 5.3: Scatter Diagram & A ; Fitted Line of Females FishRESIDUAL OUTPUT:ObservationPredicted Marine GrowthRemainders1430.3505378-25.350537752429.0145452-7.01454523430.

7958686-2.7958686024429.608319739.391680335430.9443122-6.9443122196431.3154213-29.315421267432.

5771927.4228079958428.5692143-18.569214359430.2020941-64.

2020941310429.7567633-7.75676328411431.6123085-21.612308512430.2020941-78.2020941313429.4598761-15.

4598760514430.5732032-34.5732031815430.

944312242.0556877816431.018534-33.0185340317429.

97942874.02057129118429.2372106-34.2372106219428.9403234-94.

9403233920429.311432425.6885675721427.826996311.1730037422430.573203280.4267968223427.1594.84100001224427.9754399-46.9754398825428.4207707-10.4207707326428.940323446.0596766127427.90121818.09878192728426.56522554.43477447929430.944312284.0556877830427.826996380.1730037431428.27232710.72767288532427.530109-7.53010903133426.9363346-2.93633456334425.080789430.9192106535429.162988861.8370111836429.014545244.985454837427.3816654-6.38166541438428.71765822.2823420339429.682541512.3174585240425.6745638-62.67456382

6.0 Discussion and Analysis

6.1 Discussion and analysis for All Fishs:

Figure 5.1 shows the spread diagram and fitted line of all fishes, while the drumhead end product of all fish calculated utilizing Microsoft Excel ( Data Analysis ) besides shown in subdivision 5.1. In Figure 5.1, we noticed that there is equation of consecutive line and its several coefficient of finding, R2. The value from the equation can be obtained from the drumhead end product every bit good, which is the coefficients of intercept and fresh water growing. The consecutive line equation is y = -0.2579x + 469.62 and the value of R2 = 0.0299.In order to find if I?1 differs from nothing, a trial hypothesis is done where,H0: I?1 = 0H1: I?1 a‰ 0From drumhead end product, the F in ANOVA tabular array is 2.4014. Therefore, we reject H0 if F & gt ; f0.05,1,78 a‰? 4 ( Value from brochure ) . Since F = 2.4014 & lt ; f0.05,1,78 a‰? 4, so we fail to reject H0. Therefore there is no strong grounds to find that I?1 differs from nothing.To happen 95 % assurance interval for the population mean when the fresh water growing is 100. Therefore, the fresh water growing ( x0 ) is 100, henceWhere = 469.62 and = -0.2579. Substitute these values and = 443.83.Then, by utilizing the expressionWhere t0.025,78 a‰? 2.00, = 1657.99, n = 80, = 106.5875, = 59851.3875. All values are substituted into the equation and therefore the 95 % assurance interval for the population mean is

434.46 a‰¤ a‰¤ 453.20

6.2 Discussion and analysis for Males Fish:

Figure 5.2 shows the spread diagram and fitted line of males, while the drumhead end product of males fish calculated utilizing Microsoft Excel ( Data Analysis ) besides shown in subdivision 5.2. In Figure 5.2, we noticed that there is equation of consecutive line and its several coefficient of finding, R2. The value from the equation can be obtained from the drumhead end product every bit good, which the coefficient of intercept = I?0 while the coefficient of fresh water growing = I?1.The consecutive line equation is y = -0.2364x + 478.35 and R2 = 0.0363Test hypothesis is done in order to find if I?1 differs from nothing. Therefore,H0: I?1 = 0H1: I?1 a‰ 0From the drumhead end product, the F in ANOVA tabular array is 1.4322. Therefore, we reject H0 if F & gt ; f0.05,1,38 a‰? 4.08. Since F = 1.4322 & lt ; f0.05,1,78 a‰? 4.08, so fail to reject H0. Therefore there is no strong grounds to find that I?1 differs from nothing.To obtain the 95 % assurance interval for the population mean when the fresh water growing ( x0 ) is 100, by using expressionWhere = 478.35 and = -0.2364. Substitute these values and = 454.71.Then, by utilizing the expressionWhere t0.025,38 a‰? 2.021, = 1372.83, n = 40, = 98.35, = 35169.1. All values are substituted into the equation and therefore the is within the assurance interval of

442.85 a‰¤ a‰¤ 466.57

6.3 Discussion and analysis for Females Fish:

Figure 5.3 shows the spread diagram and fitted line of males, while the drumhead end product of males fish calculated utilizing Microsoft Excel ( Data Analysis ) besides shown in subdivision 5.3. In Figure 5.3, we noticed that there is equation of consecutive line and its several coefficient of finding, R2. The value from the equation can be obtained from the drumhead end product every bit good, which the coefficient of intercept = I?0 while the coefficient of fresh water growing = I?1.The consecutive line equation is y = 0.0742x + 420.63 and R2 = 0.0016Test hypothesis is done in order to find if I?1 differs from nothing. Therefore,H0: I?1 = 0H1: I?1 a‰ 0From the drumhead end product, the F in ANOVA tabular array is 0.0614. Therefore, we reject H0 if F & gt ; f0.05,1,38 a‰? 4.08. Since F = 0.0614 & lt ; f0.05,1,78 a‰? 4.08, so we fail to reject H0. Therefore there is no strong grounds to find that I?1 differs from nothing.The 95 % assurance interval for the population mean when the fresh water growing is 100 is found by following these stairss and by utilizing expression.Where = 420.63 and = 0.0742. Substitute these values and = 428.05.Then, by utilizing the expressionWhere t0.025,38 a‰? 2.021, = 1726.24, n = 40, = 114.825, = 19253.775. All values are substituted into the equation and therefore the is within the assurance interval of

412.03 a‰¤ a‰¤ 444.07

7.0 Decision

In decision, arrested development analysis can be used to pattern the relationship between one or more response variables and one or more forecaster variables. Based on the undertaking given, it is a simple additive arrested development analysis which consists of a individual forecaster, or regressor which is the salmon ‘s growing in fresh H2O and a individual response variable which is the salmon ‘s growing in Marine. This simple additive arrested development analysis determines whether there is any relationship between the regressor and the response variable. The simple additive arrested development theoretical account besides gives a consecutive line relationship between a individual response ( dependent ) variable and a individual forecaster ( independent ) variable. In this undertaking arrested development analysis have been done individually for all fishes, males ‘ fish and females ‘ fish.From the consequences obtained, the marine growing on fresh water growing for all fishes can be represented by a consecutive line equation: . We fail to reject H0 since there is no sufficient grounds to find that I?1 differs from nothing. This shows that there is no additive relationship between the Y and X. The 95 % assurance interval for the population mean when the fresh water is 100 is between 434.46 and 453.20.On the other manus, for the males ‘ fish, is the consecutive line equation obtained from the spread diagram and fitted line secret plan. We fail to reject H0 and therefore we can statistically reason that there is no additive relationship between Y and X. The 95 % assurance interval for the population mean when the fresh water is 100 is between of 442.85 and 466.57.Besides, for females ‘ fish, the consecutive line equation obtained is. Since the H0 is failed to reject, therefore we can statistically reason that there is no additive relationship between Y and X. The 95 % assurance interval for the population mean when the fresh water is 100 is 412.03 a‰¤ a‰¤ 444.07.Last, from the consequences and analysis obtained, it is really obvious and we can therefore statically conclude that the fresh H2O growing and marine growing salmons do non hold direct relationship. This is because the informations obtained do non supply strong grounds to demo the being of one-dimensionality between fresh H2O growing and marine growing.