Casio FX 1.0 PLUS manuel d'utilisation

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Habituellement, cela est dû au manque de temps et de certitude quant à la fonctionnalité spécifique de l'équipement acheté. Malheureusement, la connexion et le démarrage Casio FX 1.0 PLUS ne suffisent pas. Le manuel d’utilisation contient un certain nombre de lignes directrices concernant les fonctionnalités spécifiques, la sécurité, les méthodes d'entretien (même les moyens qui doivent être utilisés), les défauts possibles Casio FX 1.0 PLUS et les moyens de résoudre des problèmes communs lors de l'utilisation. Enfin, le manuel contient les coordonnées du service Casio en l'absence de l'efficacité des solutions proposées. Actuellement, les manuels d’utilisation sous la forme d'animations intéressantes et de vidéos pédagogiques qui sont meilleurs que la brochure, sont très populaires. Ce type de manuel permet à l'utilisateur de voir toute la vidéo d'instruction sans sauter les spécifications et les descriptions techniques compliquées Casio FX 1.0 PLUS, comme c’est le cas pour la version papier.

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Tout d'abord, il contient la réponse sur la structure, les possibilités du dispositif Casio FX 1.0 PLUS, l'utilisation de divers accessoires et une gamme d'informations pour profiter pleinement de toutes les fonctionnalités et commodités.

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Table des matières du manuel d’utilisation

  • Page 1

    ALGEBRA FX 2.0 PLUS FX 1.0 PLUS User’s Guide 2 ( Additional Functions ) E http://world.casio.com/edu_e/[...]

  • Page 2

    CASIO ELECTRONICS CO ., L TD . Unit 6, 1000 North Circular Road, London NW2 7JD, U.K. Important! Please k eep your manual and all inf ormation handy for future ref erence.[...]

  • Page 3

    20010101 ••• ••••• •••• ••••• •• • ••••• ••••• ••••• ••• ••••• ••••• •••• ••••• ••• ••••• ••••• ••••• • •• ••••• •••• ••••• ••• ••••• ••••• [...]

  • Page 4

    20010101 Contents Chapter 1 Ad vanced Statistics Application 1-1 Adv anced Statistics (ST A T) .............................................................. 1-1-1 1-2 T ests (TEST) .................................................................................... 1-2-1 1-3 Confidence Interval (INTR) ..............................................[...]

  • Page 5

    20010101 Adv anced Statistics Application 1-1 Ad vanced Statistics (ST A T) 1-2 T ests (TEST) 1-3 Confidence Interval (INTR) 1-4 Distribution (DIST) 1 Chapter[...]

  • Page 6

    20010101 1-1 Adv anced Statistics (ST A T) u u u u u Function Menu The follo wing shows the function menus for the ST A T Mode list input screen. Pressing a function key that corresponds to the added item displays a menu that lets you select one of the functions listed below . • 3 (TEST) ... T est (page1-2-1) • 4 (INTR) ... Confidence interval [...]

  • Page 7

    20010101 • Logar ithmic Reg ression ... MSE = Σ 1 n – 2 i =1 n ( y i – ( a + b ln x i )) 2 •E xponential Repression ... MSE = Σ 1 n – 2 i =1 n ( ln y i – ( ln a + bx i )) 2 •P ow er Regression ... MSE = Σ 1 n – 2 i =1 n ( ln y i – ( ln a + b ln x i )) 2 •S in Reg ression ... MSE = Σ 1 n – 2 i =1 n ( y i – ( a sin ( bx i [...]

  • Page 8

    20010101 4. After you are finished, press i to clear the coordinate values and the pointer from the displa y . · The pointer does not appear if the calculated coordinates are not within the display range. ·T he coordinates do not appear if [Off] is specified for the [Coord] item of the [SETUP] screen. · The Y -CAL function can also be used with [...]

  • Page 9

    20010101 u u u u u Common Functions • The symbol “ ■ ” appears in the upper right cor ner of the screen while e xecution of a calculation is being performed and while a graph is being drawn. Pressing A during this time terminates the ongoing calculation or draw operation (AC Break). • Pressing i or w while a calculation result or graph is[...]

  • Page 10

    20010101 1-2 T ests (TEST) The Z T est pro vides a var iety of diff erent standardization-based tests. The y mak e it possib le to test whether or not a sample accurately represents the population when the standard deviation of a population (such as the entire population of a country) is known from previous tests. Z testing is used f or market rese[...]

  • Page 11

    20010101 The following pages e xplain various statistical calculation methods based on the principles descr ibed abov e . Details concer ning statistical principles and terminology can be found in any standard statistics textbook. On the initial ST A T Mode screen, press 3 (TEST) to display the test men u, which contains the following items. • 3 [...]

  • Page 12

    20010101 Pe rf or m the f ollowing key operations from the statistical data list. 3 (TEST) b (Z) b (1-Smpl) The following shows the meaning of each item in the case of list data specification. Data ............................ data type µ .................................. population mean v alue test conditions (“ G µ 0 ” specifies two-tail t[...]

  • Page 13

    20010101 Calculation Result Output Example µ G 11.4 ........................ direction of test z .................................. z score p .................................. p-value o .................................. mean of sample x σ n -1 ............................. sample standard deviation (Displayed only f or Data: List setting.) n ..[...]

  • Page 14

    20010101 u u u u u 2-Sample Z T est This test is used when the standard deviations f or tw o populations are known to test the h ypothesis . The 2-Sample Z T est is applied to the normal distr ib ution. Z = o 1 – o 2 σ n 1 1 2 σ n 2 2 2 + o 1 : mean of sample 1 o 2 : mean of sample 2 σ 1 : population standard deviation of sample 1 σ 2 : popul[...]

  • Page 15

    20010101 o 1 ................................. mean of sample 1 n 1 ................................. siz e (positive integer) of sample 1 o 2 ................................. mean of sample 2 n 2 ................................. siz e (positive integer) of sample 2 After setting all the parameters, align the cursor with [Execute] and then press [...]

  • Page 16

    20010101 u u u u u 1-Prop Z T est This test is used to test for an unknown proportion of successes. The 1-Prop Z T est is applied to the normal distr ibution. Z = n x n p 0 (1– p 0 ) – p 0 p 0 : e xpected sample proportion n : s i z e of sample Pe rf or m the f ollowing key operations from the statistical data list. 3 (TEST) b (Z) d (1-Prop) Pr[...]

  • Page 17

    20010101 u u u u u 2-Prop Z T est This test is used to compare the propor tion of successes. The 2-Prop Z T est is applied to the nor mal distribution. Z = n 1 x 1 n 2 x 2 – p (1 – p ) n 1 1 n 2 1 + x 1 : data value of sample 1 x 2 : data value of sample 2 n 1 : s i z e of sample 1 n 2 : s i z e of sample 2 ˆ p : estimated sample propor tion P[...]

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    20010101 p 1 > p 2 ............................ direction of test z .................................. z score p .................................. p-value ˆ p 1 ................................. estimated propor tion of sample 1 ˆ p 2 ................................. estimated propor tion of sample 2 ˆ p .................................. e[...]

  • Page 19

    20010101 k k k k k t T ests u u u u u t T est Common Functions Y ou can use the f ollowing graph analysis functions after dr a wing a g r aph. • 1 (T) ... Displa ys t score . Pressing 1 (T) di spla ys the t score at the bottom of the display , and displa ys the pointer at the corresponding location in the graph (unless the location is off the gra[...]

  • Page 20

    20010101 u u u u u 1-Sample t T est This test uses the hypothesis test for a single unkno wn population mean when the population standard deviation is unkno wn. The 1-Sample t T est is applied to t -distr ib ution. t = o – 0 µ σ x n –1 n o : mean of sample µ 0 : assumed population mean x σ n -1 : sample standard deviation n : s i z e of sam[...]

  • Page 21

    20010101 Calculation Result Output Example µ G 11.3 ...................... direction of test t ................................... t score p .................................. p-value o .................................. mean of sample x σ n -1 ............................. sample standard deviation n .................................. size of sa[...]

  • Page 22

    20010101 u u u u u 2-Sample t T est 2-Sample t T est compares the population means when the population standard deviations are unknown. The 2-Sample t T est is applied to t -distribution. The following applies when pooling is in eff ect. t = o 1 – o 2 n 1 1 + n 2 1 x p n –1 2 σ x p n –1 = σ n 1 + n 2 – 2 ( n 1 –1) x 1 n –1 2 +( n 2 ?[...]

  • Page 23

    20010101 The following shows the meaning of each item in the case of list data specification. Data ............................ data type µ 1 ................................. sample mean v alue test conditions (“ G µ 2 ” specifies two-tail test, “< µ 2 ” specifies one-tail test where sample 1 is smaller than sample 2, “> µ 2 ?[...]

  • Page 24

    20010101 Calculation Result Output Example µ 1 G µ 2 ........................... direction of test t ................................... t score p .................................. p-value df ................................. degrees of freedom o 1 ................................. mean of sample 1 o 2 ................................. mean of s[...]

  • Page 25

    20010101 u u u u u LinearReg t T est LinearReg t T est treats paired-v ar iab le data sets as ( x , y ) pairs , and uses the method of least squares to deter mine the most appropriate a , b coefficients of the data for the regression f or mula y = a + bx . It also determines the correlation coefficient and t value , and calculates the e xtent of th[...]

  • Page 26

    20010101 Calculation Result Output Example β G 0 & ρ G 0 .............. direction of test t ................................... t score p .................................. p-value df ................................. degrees of freedom a .................................. constant ter m b .................................. coefficient s ....[...]

  • Page 27

    20010101 k k k k k χ 2 T est χ 2 T est sets up a n umber of independent groups and tests h ypothesis related to the propor tion of the sample included in each group . The χ 2 T est is applied to dichotomous variab les (var iab le with tw o possible values , such as yes/no). Expected counts F ij = Σ x ij i =1 k × Σ x ij j =1 k ΣΣ i =1 j =1 x[...]

  • Page 28

    20010101 After setting all the parameters, align the cursor with [Execute] and then press one of the function k e ys shown below to perf orm the calculation or dr a w the g r aph. • 1 (CALC) ... P erforms the calculation. • 6 (DRA W) ... Draws the g r aph. Calculation Result Output Example χ 2 ................................. χ 2 val ue p ..[...]

  • Page 29

    20010101 k k k k k 2-Sample F T est 2-Sample F T est tests the h ypothesis for the ratio of sample variances . The F T est is applied to the F distr ibution. F = x 1 n –1 2 σ x 2 n –1 2 σ Pe rf or m the f ollowing key operations from the statistical data list. 3 (TEST) e (F) The following is the meaning of each item in the case of list data s[...]

  • Page 30

    20010101 After setting all the parameters, align the cursor with [Execute] and then press one of the function k e ys shown below to perf orm the calculation or dr a w the g r aph. • 1 (CALC) ... P erf or ms the calculation. • 6 (DRA W) ... Draws the g r aph. Calculation Result Output Example σ 1 G σ 2 .......................... direction of t[...]

  • Page 31

    20010101 k k k k k ANO V A ANO V A tests the hypothesis that the population means of the samples are equal when there are multiple samples. One-W ay ANO V A is used when there is one independent v ar iable and one dependent variab le . Two - Wa y ANOV A is used when there are tw o independent variab les and one dependent variab le . Pe rf or m the [...]

  • Page 32

    20010101 Calculation Result Output Example One-W ay ANO V A Line 1 (A) .................... Factor A df valu e, SS val ue , MS valu e, F value , p-value Line 2 (ERR) ............... Error df va lu e, SS val ue, MS va lue Tw o - W a y ANO V A Line 1 (A) .................... Factor A df valu e, SS val ue , MS valu e, F value , p-value Line 2 (B) ....[...]

  • Page 33

    20010101 k k k k k ANO V A (T w o-W a y) u u u u u Description The nearby tab le shows measurement results f or a metal product produced by a heat treatment process based on two treatment levels: time (A) and temper ature (B). The e xperiments were repeated twice each under identical conditions . Pe rf or m analysis of v ar iance on the f ollo wing[...]

  • Page 34

    20010101 u u u u u Input Example u u u u u Results 1-2-25 T ests (TEST)[...]

  • Page 35

    20010101 1-3 Confidence Interval (INTR) A confidence inter v al is a r ange (interv al) that includes a statistical value, usually the population mean. A confidence inter v al that is too broad makes it difficult to get an idea of where the population value (true value) is located. A narro w confidence inter val, on the other hand, limits the popul[...]

  • Page 36

    20010101 u u u u u General Confidence Interval Precautions Inputting a value in the range of 0 < C-Level < 1 for the C-Le vel setting sets you value y ou input. Inputting a v alue in the r ange of 1 < C-Lev el < 100 sets a value equiv alent to your input divided by 100. # Inputting a value of 100 or greater , or a negative value causes [...]

  • Page 37

    20010101 k k k k k Z Interval u u u u u 1-Sample Z Interval 1-Sample Z Interval calculates the confidence inter val f or an unknown population mean when the population standard deviation is kno wn. The following is the confidence interval. Left = o – Z α 2 σ n Right = o + Z α 2 σ n Ho w e ver , α is the le vel of significance. The value 100 [...]

  • Page 38

    20010101 After setting all the parameters, align the cursor with [Execute] and then press the function key shown belo w to perform the calculation. • 1 (CALC) ... P erforms the calculation. Calculation Result Output Example Left .............................. inter v al lower limit (left edge) Right ............................ inter v al upper l[...]

  • Page 39

    20010101 The following shows the meaning of each item in the case of list data specification. Data ............................ data type C-Le vel ........................ confidence level (0 < C-Lev el < 1) σ 1 ................................. population standard de viation of sample 1 ( σ 1 > 0) σ 2 ................................. [...]

  • Page 40

    20010101 u u u u u 1-Prop Z Interv al 1-Prop Z Interval uses the n umber of data to calculate the confidence inter val f or an unknown propor tion of successes. The following is the confidence interval. The v alue 100 (1 – α ) % is the confidence le vel. Left = – Z α 2 Right = + Z x n n 1 n x n x 1 – x n α 2 n 1 n x n x 1 – n :s i z e of[...]

  • Page 41

    20010101 u u u u u 2-Prop Z Interval 2-Prop Z Interval uses the n umber of data items to calculate the confidence interval for the defference between the proportion of successes in two populations. The following is the confidence interval. The v alue 100 (1 – α ) % is the confidence le vel. Left = – – Z α 2 x 1 n 1 x 2 n 2 n 1 n 1 x 1 1– [...]

  • Page 42

    20010101 Left .............................. inter v al lower limit (left edge) Right ............................ inter v al upper limit (r ight edge) ˆ p 1 ................................. estimated sample propotion for sample 1 ˆ p 2 ................................. estimated sample propotion for sample 2 n 1 ................................[...]

  • Page 43

    20010101 o .................................. mean of sample x σ n -1 ............................. sample standard deviation ( x σ n -1 > 0) n .................................. size of sample (positive integer) After setting all the parameters, align the cursor with [Execute] and then press the function key shown belo w to perform the calcul[...]

  • Page 44

    20010101 The following confidence interv al applies when pooling is not in effect. The v alue 100 (1 – α ) % is the confidence le vel. Left = ( o 1 – o 2 )– t df α 2 Right = ( o 1 – o 2 )+ t df α 2 + n 1 x 1 n –1 2 σ n 2 x 2 n –1 2 σ + n 1 x 1 n –1 2 σ n 2 x 2 n –1 2 σ C = df = 1 C 2 n 1 –1 + (1 – C ) 2 n 2 –1 + n 1 x 1[...]

  • Page 45

    20010101 o 1 ................................. mean of sample 1 x 1 σ n -1 ............................ standard deviation ( x 1 σ n -1 > 0) of sample 1 n 1 ................................. size (positive integer) of sample 1 o 2 ................................. mean of sample 2 x 2 σ n -1 ............................ standard deviation ( x[...]

  • Page 46

    20010101 1-4 Distrib ution (DIST) There is a variety of diff erent types of distr ibution, b ut the most well-known is “normal distr ib ution, ” which is essential f or perfor ming statistical calculations. Nor mal distribution is a symmetr ical distribution centered on the g reatest occurrences of mean data (highest frequency), with the freque[...]

  • Page 47

    20010101 u u u u u Common Distribution Functions After drawing a graph, you can use the P-CAL function to calculate an estimated p-value for a par ticular x va lu e. The following is the general procedure f or using the P-CAL function. 1. After dr awing a graph, press 1 (P-CAL) to display the x value input dialog bo x. 2. Input the v alue you want [...]

  • Page 48

    20010101 k k k k k Normal Distribution u u u u u Normal Probability Density Nor mal probability density calculates the probability density of nomal distribution from a specified x value. Nor mal probability density is applied to standard nor mal distribution. πσ 2 f ( x ) = 1 e – 2 2 σ ( x – µ ) 2 µ ( σ > 0) Pe rf or m the f ollowing k[...]

  • Page 49

    20010101 u u u u u Normal Distribution Pr obability Nor mal distrib ution probability calculates the probability of nor mal distribution data f alling between two specific values. πσ 2 p = 1 e – dx 2 2 σ ( x – µ ) 2 µ a b ∫ a : lo w er boundar y b : upper boundar y Pe rf or m the f ollowing key operations from the statistical data list. [...]

  • Page 50

    20010101 Calculation Result Output Example p .................................. nor mal distribution probability z:Low ........................... z:Low value (con ver ted to standardize z score for lower value) z:Up ............................. z:Up value (conv er ted to standardize z score for upper v alue) u u u u u In verse Cumulative Normal D[...]

  • Page 51

    20010101 After setting all the parameters, align the cursor with [Execute] and then press the function key shown belo w to perform the calculation. • 1 (CALC) ... P erforms the calculation. Calculation Result Output Examples x ....................................... inverse cum ulativ e nor mal distr ibution (T ail:Left upper boundar y of integ r[...]

  • Page 52

    20010101 k k k k k Student- t Distribution u u u u u Student- t Pr obability Density Student- t probability density calculates t probability density from a specified x va lu e. f ( x ) = Γ Γ df π – df + 1 2 2 df 2 df + 1 df x 2 1+ Pe rf or m the f ollowing key operations from the statistical data list. 5 (DIST) c (T) b (P .D) Data is specified[...]

  • Page 53

    20010101 u u u u u Student- t Distribution Pr obability Student- t distrib ution probability calculates the probability of t distr ib ution data f alling between two specific values. p = Γ Γ df π 2 df 2 df + 1 – df +1 2 df x 2 1+ dx a b ∫ a :l ow er boundar y b : upper boundary Pe rf or m the f ollowing key operations from the statistical da[...]

  • Page 54

    20010101 Calculation Result Output Example p .................................. Student- t distrib ution probability t:Lo w ........................... t:Low v alue (input lower v alue) t:Up ............................. t:Up v alue (input upper v alue) k k k k k χ 2 Distribution u u u u u χ 2 Pr obability Density χ 2 probability density calcula[...]

  • Page 55

    20010101 Calculation Result Output Example p .................................. χ 2 probability density # Current V -Window settings are used f or graph drawing when the SET UP screen's [Stat Wind] setting is [Manual]. The V - Window settings below are set automatically when the [Stat Wind] setting is [A uto]. Xmin = 0, Xmax = 11.5, Xscale = [...]

  • Page 56

    20010101 u u u u u χ 2 Distrib ution Probability χ 2 distribution probability calculates the probability of χ 2 distribution data falling betw een two specific values. p = Γ 1 2 df df 2 x e dx 2 1 df 2 –1 x 2 – a b ∫ a :l ow er boundar y b : upper boundary Pe rf or m the f ollowing key operations from the statistical data list. 5 (DIST) d[...]

  • Page 57

    20010101 Calculation Result Output Example p .................................. χ 2 distribution probability k k k k k F Distrib ution u u u u u F Probability Density F probability density calculates the probability density function f or the F distr ib ution at a specified x va lu e. Γ n 2 x d n n 2 – 1 2 n Γ 2 n + d Γ 2 d d nx 1 + n + d 2 f [...]

  • Page 58

    20010101 Calculation Result Output Example p .................................. F probability density # V-Windo w settings f or graph dr awing are set automatically when the SET UP screen's [Stat Wind] setting is [A uto]. Current V - Window settings are used for graph drawing when the [Stat Wind] setting is [Manual]. 1-4-13 Distribution (DIST)[...]

  • Page 59

    20010101 u u u u u F Distribution Pr obability F distribution probability calculates the probability of F distr ib ution data falling between two specific values. p = Γ n 2 dx x d n n 2 –1 2 n Γ 2 n + d Γ 2 d d nx 1 + n + d 2 – a b ∫ a : lower boundary b : upper boundar y Pe rf or m the f ollowing key operations from the statistical data l[...]

  • Page 60

    20010101 Calculation Result Output Example p .................................. F distribution probability 1-4-15 Distribution (DIST)[...]

  • Page 61

    20010101 k k k k k Binomial Distribution u u u u u Binomial Probability Binomial probability calculates a probability at a specified value for the discrete binomial distr ib ution with the specified number of tr ials and probability of success on each trial. f ( x ) = n C x p x (1– p ) n – x ( x = 0, 1, ·······, n ) p : success probabili[...]

  • Page 62

    20010101 Calculation Result Output Example p .................................. binomial probability u u u u u Binomial Cumulative Density Binomial cumulative density calculates a cumulative probability at a specified v alue f or the discrete binomial distribution with the specified number of tr ials and probability of success on each tr ial. Pe rf[...]

  • Page 63

    20010101 After setting all the parameters, align the cursor with [Execute] and then press the function key shown belo w to perform the calculation. • 1 (CALC) ... P erforms the calculation. Calculation Result Output Example p ......................................... probability of success 1-4-18 Distribution (DIST) 20011101[...]

  • Page 64

    20010101 k k k k k Po isson Distribution u u u u u Po isson Probability Po isson probability calculates a probability at a specified value for the discrete P oisson distribution with the specified mean. f ( x ) = x! e – x µ µ ( x = 0, 1, 2, ···) µ :m ean ( µ > 0) Pe rf or m the f ollowing key operations from the statistical data list. 5[...]

  • Page 65

    20010101 u u u u u P oisson Cumulative Density Po isson cumulativ e density calculates a cumulativ e probability at specified value for the discrete Poisson distribution with the specified mean. Pe rf or m the f ollowing key operations from the statistical data list. 5 (DIST) g (P oissn) c (C .D) The following shows the meaning of each item when da[...]

  • Page 66

    20010101 k k k k k Geometric Distrib ution u u u u u Geometric Probability Geometr ic probability calculates the probability at a specified v alue, and the number of the trial on which the first success occurs, for the geometr ic distrib ution with a specified probability of success. f ( x ) = p (1– p ) x – 1 ( x = 1, 2, 3, ···) Pe rf or m t[...]

  • Page 67

    20010101 u u u u u Geometric Cumulative Density Geometr ic cumulativ e density calculates a cumulative probability at specified value , the nu mber of the trial on which the first success occurs, f or the discrete geometr ic distr ib ution with the specified probability of success. Pe rf or m the f ollowing key operations from the statistical data [...]